BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.earthmonitor.org//
BEGIN:VTIMEZONE
TZID:Europe/London
BEGIN:STANDARD
DTSTART:20001029T020000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T010000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-gw2023-C7SCWP@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231004T141500
DTEND;TZID=Europe/London:20231004T144500
DESCRIPTION:The era of open Earth Observation (EO) data started 2008 when t
 he United States Geological Survey (USGS) made the Landsat archive availab
 le free-of-charge. Since then\, the amount of open EO data has increased e
 xponentially\, also due to Copernicus\, the European Union’s Earth Obser
 vation programme. The current wealth of open EO data available is unpreced
 ented. This leads to the current situation that a considerable fraction of
  the open EO data produced and disseminated on a daily basis is not used a
 s users cannot access\, process and analyse the data. Questions on how EO 
 data can be utilised to better support the Green New Deal and related comm
 unities\, such as Renewable Energies\, lead the discussion now and will do
  so in the coming years. A better understanding of the needs and requireme
 nts of different users (from EO data users to policy- and decision-makers)
  will be vital in shaping the future of open EO data.\n\n“To create the 
 future\, we must understand the past” is a famous quote stated by astrop
 hysicist Dr. Carl Sagan. Hence\, in this keynote talk\, I would like to ta
 ke the audience on a time travel through the era of open EO data. We will 
 take the perspective of an EO data user and first identify key milestones 
 and developments since 2008 before we draw a more detailed picture of what
  it is like at the moment to discover\, access\, process and retrieve know
 ledge from open EO data. After we shed a light on the past and the present
 \, time travel continues to the year 2030 and together with the audience\,
  I’d like to develop a wish list on how open EO data shall be of value f
 or different stakeholders in the future and extract the key requirements t
 hat are needed to achieve this.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Open Earth Observation - Shaping the future by understanding the pa
 st - Julia Wagemann (PhD)
URL:https://pretalx.earthmonitor.org/gw2023/talk/C7SCWP/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-VXZTAJ@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231004T144500
DTEND;TZID=Europe/London:20231004T151500
DESCRIPTION:Open Science is increasingly recognized as a catalyst for innov
 ation. Back in 2016\, the EC's DG-RTD laid a vision for European R&D which
  acknowledged that “the way that science works is fundamentally changing
 \, and an equally important transformation is taking place in how companie
 s and societies innovate. The advent of digital technologies is making sci
 ence and innovation more open\, collaborative\, and global”. The concept
  of Open Science and Innovation is embraced by the European Space Agency i
 n \nits Agenda 2025\, recognizing the value that such principles of innova
 tion can bring for the space sector in terms of optimizing development cyc
 les\, accelerating time to market\, and reducing cost. \nIn this talk we w
 ill present ways in which Open Science and Innovation are addressed in ESA
 's Earth Observation Programme\, and how such principles are transferred i
 nto R&D and Scientific activities. We will dive into the "WHYs" and "WHENs
 " of adopting openness in the Earth Observation value chain\, share lesson
 s learned from community consultations and finally look into the "HOWs" of
  implementing Open Science and Innovation in EO building on elements of ag
 ility\, sustainability and scalability provided by incorporation of new di
 gital technologies and design-driven product development and science for s
 ociety.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Earth Observation Open Science and Innovation - Anca Anghelea
URL:https://pretalx.earthmonitor.org/gw2023/talk/VXZTAJ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-MDS8D9@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231004T154500
DTEND;TZID=Europe/London:20231004T160500
DESCRIPTION:The Group on Earth Observations (GEO) envisions a future where 
 decisions and actions for the benefit of humankind are informed by coordin
 ated\, comprehensive and sustained Earth observations. A central part of G
 EO’s Mission is to build a Global Earth Observation System of Systems (G
 EOSS)\, a set of coordinated\, independent Earth observation\, information
  and processing systems that interact and provide access to diverse inform
 ation for a broad range of users. The amounts of Earth observation data ar
 e increasing drastically over the last years\, as they include different d
 ata sources from satellites to model outputs\, from airborne sensors to gr
 ound stations and in-situ data. The main challenge is how to find and make
  use of these resources by users worldwide. \nThe European Space Agency\, 
 together with the Italian National Research Council and the University of 
 Geneva are contributing to the implementation of GEOSS via the EU H2020 co
 -funded project GPP (GEOSS Platform Plus). This European contribution is r
 einforcing the use of Earth Observations globally focusing on provisioning
  of actionable information for climate change research\, monitoring\, and 
 development of mitigation and adaptation actions. A user centric approach 
 helps to focus on real user needs\, listening and co-designing new impleme
 ntations in close coordination with users. Examples of applications target
 ed include SDG 15.3.1 on Land Degradation\, Climate Change impact on Norov
 irus Pandemic Risk\, and SDG11.7 that relates to climate change\, urban su
 stainability and health. Access to data products\, services and informatio
 n\, and the possibility to generate actionable information to derive resul
 ts as input to decision makers are main objectives. Relevant contributions
  to an evolved overarching GEOSS architecture that copes with changes in t
 he GEO landscape\, including general changes in the Science/Policy landsca
 pe in technological innovation are foreseen as well. In this context\, GPP
  is playing an important role in connecting application developers (scient
 ists\, developers) to providers (of data\, platforms\, services\, etc.) fo
 r enabling the generation of actionable information usable by the end user
 s (decision makers\, institutions\, citizens). It will be then presented s
 ome real cases of how GEO communities and providers could contribute to GE
 OSS and how users can benefit from the tools and applications available in
  the GEOSS Ecosystem to support the different actors in different GEO incu
 bator areas (also known as societal benefit areas).
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:GEOSS Platform Plus: European efforts to reinforce the use of Earth
  Observations globally - Alessandro Scremin
URL:https://pretalx.earthmonitor.org/gw2023/talk/MDS8D9/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-8TCDLN@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231004T154500
DTEND;TZID=Europe/London:20231004T160500
DESCRIPTION:Landsat is the longest running program to provide space-based d
 ata for Earth’s land surface. Based on nine satellites\, the program has
  been monitoring the planet since 1972\, consistently providing multi spec
 tral images for several applications. Due to technology differences among 
 the satellites / image sensors\, the reflectance values may have significa
 nt variations across the entire time-series. Data gaps\, due cloud cover\,
  and stripe artifacts\, caused by the Scan Line Corrector failure (Landsat
  7)\, add an additional level of complexity for the users interested in pe
 rform long-term time series analysis and machine learning on this data. Co
 nsidering these challenges and the potential of usability of the Landsat i
 magery\, here we presented a workflow to produce analysis-ready and cloud-
 optimized (ARCO) global mosaics including: 1) data harmonization\, 2) clou
 d and artifact screening\, 3) temporal aggregation\, 4) gapfilling\, and 5
 ) mosaicking. Relying on de-facto standards (Cloud-Optimized GeoTIFF - COG
  and SpatioTemporal Asset Catalog - STAC)\, the Landsat global ARCO mosaic
 s have potential to boost the access of Landsat data and contribute with t
 he monitoring of land use conversion\, food production\, biodiversity\, cl
 imate change and land productivity.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Conference Hall
SUMMARY:Global Analysis-Ready and Cloud-Optimized (ARCO) Landsat Mosaics: C
 hallenges and implementation strategies - Leandro Leal Parente
URL:https://pretalx.earthmonitor.org/gw2023/talk/8TCDLN/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-VDFCUV@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231004T154500
DTEND;TZID=Europe/London:20231004T163000
DESCRIPTION:Reproducibility and Reusability of workflows are increasingly i
 mportant topics in Remote Sensing research when moving towards FAIR and op
 en data science. This workshop discusses the current status quo\, and how 
 we can improve this with future activities.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Seminar room 2 & 3
SUMMARY:Reproducible and Reusable Remote Sensing Workflows - Edzer Pebesma
URL:https://pretalx.earthmonitor.org/gw2023/talk/VDFCUV/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-ELM7EN@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231004T161000
DTEND;TZID=Europe/London:20231004T163000
DESCRIPTION:The Copernicus Data Space Ecosystem (CDSE) represents a key mil
 estone in access to Copernicus satellite data. First and foremost\, the no
 velty relates to the paradigm shift that all Copernicus data (except for s
 ome of the raw data) is immediately available online - global coverage and
  the entire time range including the archive - at no cost\, for any user. 
 The product list includes Copernicus satellite imagery (Sentinel-1\, Senti
 nel-2\, Sentinel-3\, Sentinel-5p)\, Copernicus Services and other satellit
 e data missions (e.g. Landsat\, SMOS\, Envisat). In the orderable mode\, h
 istorical Sentinel-1 RAW data and processing of Sentinel 1/2/3 data using 
 official ESA processors is available. So-called Sentinel Engineering Data 
 (mostly Level-0 data) will be available in the rolling 2-week archive. Mor
 eover\, CDSE will offer access to commercial satellite data.\n\nThe main a
 dvantage of immediately available data is that the user does not have to o
 rder and wait for the data. Direct access also allows bulk data processing
  and streaming\, e.g. via OGC services (WMS/WFS). In this respect\, only o
 nline data access provides sufficient capacity to visualise data online. A
 nother advantage of immediately available data is the ability to partially
  read large data files if they are stored in an optimised chunked format s
 uch as Cloud Optimised GeoTIFF (COG) or Zarr for rasters\, or GeoParquet f
 or vectors. Partial reading is essential for parallel computing\, which al
 lows small chunks of data to be processed in parallel so that there is no 
 need to wait for the data to be fully loaded before processing.\n\nAnother
  novelty of CDSE are the various interfaces where the data are available: 
 from old-fashioned download to various interfaces providing capability to 
 search the catalog connected to the same database to guarantee consistency
 . First interface is OData - an ESA-adopted standard\, which is based on h
 ttps RESTful Application Programming Interfaces. It enables resources\, wh
 ich are identified by URLs and defined in a data model\, to be created and
  edited using simple HTTP messages.  Another interface which is foreseen i
 s the STAC catalogue and API that become a standard in the EO community\, 
 also being onboarded to OGC. The CDSE also provides Jupyter Hub - a very s
 uitable tool for prototyping\, developing\, and testing applications for E
 arth Observation data processing. This is an open-source\, online\, intera
 ctive web application which gives access to computational environments and
  resources without burdening the users with installation and maintenance t
 asks.\n\nThe vast majority of the described capabilities are available fre
 e-of-charge for the individual's use - personal\, research or commercial. 
 For those interested in larger scale processing\, there are practically un
 limited resources available under commercial terms.  The first one of thes
 e is CREODIAS\, which allows user to access and process the data directly 
 from federated cloud environment\, order serverless processing of the EO p
 roducts and access EO-dedicated services. Yet\, additional third-party pro
 viders are joining CDSE to offer a variety of additional services (free an
 d commercial) as members of the Data Space.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:COPERNICUS DATA SPACE ECOSYSTEM: IMMEDIATELY AVAILABLE DATA AND ASS
 OCIATED SERVICES - Jędrzej Bojanowski
URL:https://pretalx.earthmonitor.org/gw2023/talk/ELM7EN/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-9BHPU8@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231004T161000
DTEND;TZID=Europe/London:20231004T163000
DESCRIPTION:The available information for monitoring the Earth has never be
 en as abundant and accessible as today. Data volumes from Earth Observatio
 n\, mathematical models and in situ measurements will continue to grow in 
 the future. While this data richness opens unprecedented possibilities for
  monitoring and developing a holistic understanding of our planet\, it pos
 es challenges with respect to efficient data exploitation and fosters nove
 l technological approaches for the joint exploitation of the different dat
 a streams. Despite considerable efforts for standardisation by different i
 nstitutions in the past\, data formats and models as well as interfaces fo
 r data access remain diverse\, resulting in costly\, bespoke solutions for
  finding\, accessing\, and processing heterogeneous input data. The xcube 
 open-source Python package addresses such requirements and offers a suite 
 of comprehensive tools for transforming data sets into analysis-ready data
  cubes. As it is designed as a framework\, it is continuously extended to 
 cater to changing needs. It integrates seamlessly into Python’s data sci
 ence stack\, as advocated by Pangeo\, and extends it by specific data stor
 es for the access to relevant data sets for Earth System Science and tools
  for data provisioning and exploitation.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Conference Hall
SUMMARY:Monitoring the Earth System with xcube - Gunnar Brandt\, Tonio Finc
 ke
URL:https://pretalx.earthmonitor.org/gw2023/talk/9BHPU8/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-7QUHNY@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231004T163000
DTEND;TZID=Europe/London:20231004T171500
DESCRIPTION:Spatiotemporal data cubes are becoming ever more abundant and a
 re a widely used tool in the Earth System Science community to handle geos
 patial raster data. \nSophisticated frameworks in high-level programming l
 anguages like R and python allow scientists to draft and run their data an
 alysis pipelines and to scale them in HPC or cloud environments. \n\nWhile
  many data cube frameworks can handle harmonized analysis-ready data cubes
  very well\, we repeatedly experienced problems when running complex analy
 ses on multi-source data that was not homogenized. The problems arise when
  different datasets need to be resampled on the fly to a common resolution
  and have non-aligning chunk boundaries\, which leads to very complex and 
 often unresolvable task graphs in frameworks like xarray+dask.\n\nIn this 
 workshop we present the emerging ecosystem of large-scale geodata processi
 ng in the Julia programming language under the JuliaDataCubes github umbre
 lla. \nJulia is an interactive scientific programming language\, designed 
 for HPC applications with primitives for Multi-threaded and Distributed co
 mputations built into the language. \nWe will demonstrate an example analy
 sis where data from different sources (global fields of daily MODIS\, hour
 ly ERA5\, high-resolution land cover)\, summing to multiple TBs of data\, 
 can interoperate on-the-fly and scale well when run on different computing
  environments.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Seminar room 2 & 3
SUMMARY:Distributed computing on large geodata from multiple sources using 
 the Julia Programming language - Felix Cremer\, Daniel Loos
URL:https://pretalx.earthmonitor.org/gw2023/talk/7QUHNY/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-LXPPYZ@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231004T163500
DTEND;TZID=Europe/London:20231004T165500
DESCRIPTION:The European Union's Green Deal is a transformative initiative 
 aimed at achieving climate neutrality and sustainable economic growth by 2
 050. To address the complex challenges posed by climate change and environ
 mental degradation\, a robust infrastructure of platform services has emer
 ged to support the Green Deal. At EODC\, we implement and develop key comp
 onents of this infrastructure\, including the openEO Platform\, ESA GTIF A
 ustria\, STAC\, Pangeo\, and the GREAT project. These  services and projec
 ts play a pivotal role in gathering\, processing\, and disseminating criti
 cal environmental data and insights that drive policy formulation and sust
 ainable practices.\n\nThe openEO Platform serves as a cornerstone for the 
 Green Deal by providing standardized and open interfaces for accessing and
  processing Earth Observation (EO) data. Leveraging cloud computing and di
 stributed resources\, openEO enables researchers\, policymakers\, and busi
 nesses to harness the power of petabytes of environmental data. By fosteri
 ng collaboration and interoperability\, openEO supports the development of
  innovative applications and services that contribute to the Green Deal's 
 goals\, such as carbon monitoring and land-use planning.\n\nThe SpatioTemp
 oral Asset Catalog (STAC) standardises the organisation and discovery of g
 eospatial assets\, making it easier to find and use EO data. By embracing 
 STAC\, the Green Deal ecosystem and platforms ensure that valuable environ
 mental information\, such as satellite imagery and climate models\, can be
  efficiently located and employed in various applications\, including envi
 ronmental monitoring and disaster risk management.\n\nPangeo\, an open-sou
 rce platform for scalable Earth science\, empowers researchers with tools 
 and resources for analysing massive climate and environmental datasets. By
  offering Jupyter notebooks\, cloud computing\, and data catalogues\, Pang
 eo facilitates collaborative research and data-driven decision-making. Pan
 geo's contributions are vital to the scientific underpinnings of the Green
  Deal\, assisting in climate modelling\, biodiversity assessment\, and eco
 logical forecasting.\n\nESA's Green Transition Information Factory (GTIF) 
 demonstrates in Austria how to enhance the visibility and access to EO-der
 ived datasets and knowledge for decision-makers\, policymakers and citizen
 s. The platform additionally showcases how to efficiently disseminate info
 rmation for Austria's green transition initiatives allowing users to disco
 ver the potentials of transitioning to carbon neutrality by 2050 through E
 O data and cloud computing.\n\nThe GREAT project is focused on building a 
 robust data infrastructure for the Green Deal. It aims to integrate data f
 rom various sources\, including EO\, environmental sensors\, and socioecon
 omic indicators\, into a comprehensive data space. This unified data repos
 itory enhances data sharing and accessibility\, supporting the monitoring\
 , evaluation\, and adaptation of Green Deal policies and actions.\n\nIn co
 nclusion\, these projects form a cohesive and adaptable infrastructure tha
 t underpins the Green Deal's ambition to combat climate change and promote
  sustainability. By providing open and standardized interfaces\, advanced 
 geospatial capabilities\, efficient data organization\, scalable computing
 \, and comprehensive data integration\, these platforms facilitate data-dr
 iven decision-making\, innovation\, and drive progress toward the EU's env
 ironmental goals. In the face of the global climate crisis\, these service
 s are central tools in advancing the Green Deal's mission to build a green
 er and more sustainable future for all.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:European platforms to support the Green Deal - Benjamin Schumacher
URL:https://pretalx.earthmonitor.org/gw2023/talk/LXPPYZ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-3WDNHJ@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231004T163500
DTEND;TZID=Europe/London:20231004T165500
DESCRIPTION:In tandem with the monumental increase in geo-data availability
  from remote sensors\, field sensors and various publicly available enviro
 nmental datasets\, state-of-the-art geoinformatics algorithms have evolved
  to harness earth science data as never before. In the field of computatio
 nal hydrology\, these processes have yielded global information in fine de
 tail\, and of exceptional precision.\n\nHydrography90m is one such data pr
 oduct that pushes the boundaries of computational hydrology in several way
 s. It is a globally standardised and seamless hydrographic dataset that al
 lows the mapping of headwaters in unprecedented density and detail. With t
 he minimum upstream contributing area set at 0.05km^2\, it comprises the h
 ighest density of headwaters compared to leading global hydrographic asses
 sments. The dataset contains 1.6 million drainage basins and 726 million s
 tream segments and sub-catchments. It is also designed to overcome the spa
 tial and accessibility constraints of gauged locations and address the lim
 itations of spectral analyses. \n\nAs for applications in scientific resea
 rch\, Hydrography90m is well-suited for both global and comparative area-o
 f-interest studies. The dataset contains many essential stream features\, 
 such as stream slope\, stream distance\, types of stream order and flow in
 dices. Hydrography90m thus offers significant utility in the assessment of
  freshwater quantity and quality\, inundation risk\, biodiversity and cons
 ervation\, and resource management objectives\, all in a globally comprehe
 nsive and standardised manner. \n\nIn terms of the underlying computationa
 l approach\, Hydrography90m is based on a drainage flow algorithm that dis
 tributes downhill water flow in a realistic manner\, following the concavi
 ty and convexity of terrain. Additionally\, programming in a variety of op
 en \nsource software provides unmatched computational power\, and the impl
 ementation of different scripting procedures allows for bench-marking stra
 tegies to check for potential errors. Software employed includes GDAL\, Pk
 tools\, and GRASS GIS.\n\nThe novel computational approach of Hydrography9
 0m broadens the scope for using various remote and field sensor technologi
 es\, and the scripting procedure lays the foundation for more complex Mach
 ine Learning-based discharge assessments. Its design is a pivotal developm
 ent for addressing the challenges of overfitting and universal coverage in
  hydrological modelling. Machine Learning can now enable the massive data 
 integration that is vital for global scale hydrological studies\; and hydr
 ographic data with fine detail of headwaters provides an excellent foundat
 ion for interbasin connectivity and high-resolution discharge predictions.
  Meanwhile\, other data-driven and ensemble methods that have emerged rece
 ntly to address these technical challenges still remain limited as tools f
 or basin-specific studies.\n\nGiven the multitude of resource and conserva
 tion applications\, Hydrography90m can be a vital toolkit for achieving se
 veral UN Sustainable development goals. Additional uses of the dataset are
  relevant to freshwater flows and sediment transport computations\, pollut
 ant and nutrient concentration assessments\, public health\, and geopoliti
 cal and resource challenges. To date\, Hydrography90m has been used in spe
 cies distribution modelling for aquaculture\, vector-borne disease mapping
 \, and various ecological studies. Institutions engaged in water resource 
 management\, transnational security and environmental crime monitoring are
  also starting to derive value from the dataset’s attributes.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Conference Hall
SUMMARY:The Hydrography90m dataset: present offering and future scope - Tus
 har Sethi
URL:https://pretalx.earthmonitor.org/gw2023/talk/3WDNHJ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-TE7TSF@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T090000
DTEND;TZID=Europe/London:20231005T093000
DESCRIPTION:DestinE Core Service Platform integrates and operates an open e
 cosystem of services (also referred to as DESP Framework) to support Desti
 nE-data exploitation and information sharing for the benefit of DestinE us
 ers and Third-Party entities.\nDESP includes key essential services such a
 s user management service\; infrastructure as a service with storage\, net
 work\, and CPU/GPU capabilities\; data access and retrieval service\, in p
 articular from the DestinE Data Lake operated by EUMETSAT\, as it is the b
 ackbone for the data generated by the ECMWF’s Digital Twin Engine\; data
  traceability and harmonization services\; basic software suite service fo
 r local data exploitation\; data and software catalogue services\; and 2D/
 3D data visualization service.\nDESP additionally provides onboarding supp
 ort for integrating external services and resources\, making the ecosystem
  flexible\, scalable\, and easily adapted to the community needs. DestinE 
 ecosystem aims to support the needs of a large and diverse community of us
 ers including general citizens\, scientists and academics\, commercial ent
 ities\, or policy makers. DESP Framework defines the required conditions f
 or a service to be part of the ecosystem\, and therefore benefit of the av
 ailable resources\, and of the potential to engage with all DestinE users.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:DestinE Core Service Platform - Inés Sanz Morère
URL:https://pretalx.earthmonitor.org/gw2023/talk/TE7TSF/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-XSRUHC@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T093000
DTEND;TZID=Europe/London:20231005T100000
DESCRIPTION:The concept of “Analysis Ready Data” (ARD) was initially de
 veloped around 2015 within the Committee on Earth Observation Satellite (C
 EOS). CEOS defines ARD as “satellite data that have been processed to a 
 minimum set of requirements and organized into a form that allows immediat
 e analysis with a minimum of additional user effort and interoperability b
 oth through time and with other datasets”. Over the course of the past f
 ew years CEOS has issued a number of so-called ‘Product Family Specifica
 tions’ (PFS) which cover a variety of different sensing methods and obse
 rved parameters. Institutional and commercial satellite data providers hav
 e accepted these specifications and by now a broad variety of satellite im
 age products are available as “CEOS-ARD certified”.\nThe popularity of
  the concept and the expectation for the ARD ‘label’ in terms of simpl
 ified usability and interoperability of a wealth of EO data has spurred th
 e desire to expand ARD beyond classical EO parameters by including higher 
 level products and to cover geospatial data more generically. This prompte
 d a discussion on which categories\, levels\, or classes of analysis ready
  data are needed and how they could be defined and distinguished. These co
 nsiderations are now taken up by a formal ISO/OGC Standard Working Group (
 SWG) which was launched recently.\nIn the meantime\, the amount of availab
 le geospatial data increases exponentially and many of these are available
  free&open and calling themselves ‘ARD’. However\, users relying on th
 eir interoperability are often overwhelmed by their diversity and remainin
 g inconsistencies which often require considerable effort before appropria
 te data can be selected and joined sensibly. Access to proper reference da
 ta and benchmarking methods is therefore an important factor for leveragin
 g ‘ARD’ in the future.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Analysis Ready Data - Peter Strobl
URL:https://pretalx.earthmonitor.org/gw2023/talk/XSRUHC/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-NHHBUW@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T100000
DTEND;TZID=Europe/London:20231005T103000
DESCRIPTION:The OpenClimate Network is an open source nested accounting pla
 tform allows users to navigate emissions inventories and climate pledges o
 f different actors at every level\, aggregating data from various public s
 ources for countries\, regions\, cities and companies. Through this aggreg
 ation\, it enables the comparison of how different data sources report emi
 ssions of certain actors\, by harmonizing the way data is reported and ide
 ntifying the different methodologies used.\n\nAdditionally\, by nesting ac
 tors into their respective jurisdictions it facilitates the comparison bet
 ween the pledges these actors have committed to\, and to see if they are a
 ligned towards the same climate targets\, and how these compare to the goa
 ls of the Paris Agreement.\n\nBy aggregating data and exploring it in this
  nested manner\, it also allows for the effective identification of data g
 aps for these actors\, suggesting where efforts are needed to identify exi
 sting data sources or help produce new inventories. When data gaps are ide
 ntified\, the platform also prompts users to contribute data based on the 
 open and standardized data model used to aggregate emissions and pledges d
 ata.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Aggregating multiple emissions data for actors at all levels throug
 h a common schema in OpenClimate - Joaquin van Peborgh
URL:https://pretalx.earthmonitor.org/gw2023/talk/NHHBUW/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-HR9NR9@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T103500
DTEND;TZID=Europe/London:20231005T104000
DESCRIPTION:The use of synthetic aperture radar (SAR) data has become incre
 asingly important in remote sensing for environmental monitoring. SAR data
  provides valuable information on surface characteristics and changes\, su
 ch as land cover and land use change\, soil condition\, and vegetation gro
 wth\, making it a powerful tool for various applications\, including agric
 ulture\, forestry\, and climate change studies. However\, processing and i
 ntegrating SAR data into analysis-ready formats can be complex and time-co
 nsuming\, requiring specialized knowledge and tools.\nIn this contribution
 \, we propose a software project called force-sar\, which aims to integrat
 e Sentinel-1 data into the Framework for Operational Radiometric Correctio
 n for Environmental monitoring (FORCE). FORCE is a widely used data cube f
 ramework for processing and analyzing optical remote sensing data and the 
 integration of SAR data into FORCE increases its capabilities for large-sc
 ale\, multi-modal analysis.\nForce-sar automatically queries available Sen
 tinel-1 Ground Range Detected (GRD) imagery covering the spatial and tempo
 ral dimensions of your area of interest. The scenes are then directly acce
 ssed from satellite data repositories on cloud environments like Creodias 
 or the Copernicus Data and Exploitation Platform Germany (CODE-DE). When n
 o connection to such repositories is available\, the data can also be down
 loaded from data centers like the Alaskan Satellite Facility (ASF). The sc
 enes are processed to radiometrically calibrated gamma-naught backscatter 
 data using a pre-built but customizable ESA SNAP processing graph. After r
 esampling\, reprojecting\, and tiling the data\, they are ready for ingest
 ion into a FORCE data cube.\nThe integration of Sentinel-1 data into FORCE
  allows for the creation of SAR data cubes with consistent radiometric and
  geometric properties\, covering large regions and spanning multiple time 
 periods. This enables users to perform multi-sensor and multi-temporal ana
 lyses like change detection\, compositing\, and classification and regress
 ion tasks for environmental monitoring at large scales\, thereby supportin
 g decision-making and policy evaluation in frameworks like the EUs Green D
 eal or the Common Agricultural Policy.\nForce-sar is already used in the o
 ngoing Mowing Detection Intercomparison Exercise (MODCiX)\, where more tha
 n ten teams compare their algorithms for grassland mowing detection on a c
 onsistent and harmonized remote sensing and reference data set. A consiste
 nt data cube holding optical and SAR data covering test regions in eight E
 uropean countries was created and is used for the study.\nForce-sar provid
 es a streamlined and fully containerized workflow for preprocessing and in
 tegrating SAR data into an existing data cube framework without the need f
 or the installation of any external tools or dependencies. This opens up n
 ew possibilities for utilizing SAR data in large-scale environmental monit
 oring applications\, particularly when working in cloud environments.
DTSTAMP:20260415T003046Z
LOCATION:Poster presentation
SUMMARY:Integrating Sentinel-1 Data into FORCE for Large-Area Analysis Read
 y SAR Data Cubes - Felix Lobert\, Tom Broeg
URL:https://pretalx.earthmonitor.org/gw2023/talk/HR9NR9/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-73NPHA@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T104000
DTEND;TZID=Europe/London:20231005T104500
DESCRIPTION:Earth System Models and Earth Observations are crucial for stud
 ying the Earth\, providing scientific insight into fundamental dynamics an
 d valuable predictions about Earth’s future. However\, they generate hug
 e amounts of data\, at different temporal and spatial scales\, so it becom
 es of paramount importance to access them in a seamless and efficient way 
 for scientific analysis. Usually Earth Science datasets are represented wi
 th hundreds or thousands of files that can introduce a lot of burden to th
 e user in terms of management\, since it requires the user to set up compu
 tational and storage resources for accessing and retrieving data and writi
 ng code to load and prepare data into in-memory data structures for analys
 is.\n\nIn this talk\, we describe in detail the architectural design\, imp
 lementation and deployment of a data management and analytics system in or
 der to facilitate cataloguing\, accessing and processing Earth Science dat
 a. The system has been designed using a cloud-native architecture\, based 
 on containerized microservices\, that facilitates the development\, deploy
 ment and maintenance of the system itself. It has been implemented by inte
 grating different open source frameworks\, tools and libraries and has bee
 n deployed using the Kubernetes platform and related tools such as kubectl
  and kustomize.\n\nThe Data Platform consists of different components that
  will be introduced and described together with the related technologies a
 dopted: (a) the Catalog\, based on Intake and MongoDB for cataloguing and 
 indexing the datasets published and managed in the system\, (b) the Analyt
 ics Engine\, based on the geokube and dask Python libraries: geokube is us
 ed for specialised geospatial operations (such as extracting a bounding bo
 x or a multipolygon) according to different types of geoscientific dataset
 s and dask for parallel and distributed processing\; (c) the Broker implem
 ented using RabbitMQ framework for managing the user workload requests\; f
 inally\, (d) the Rest APIs and the OGC standard interfaces (i.e.\, WPS) to
  access data and to submit analytics workflows.\n\nAn instance of the Data
  Platform has been deployed in production at Euro-Mediterranean Centre on 
 Climate Change (CMCC) for the delivery and analysis of data produced by th
 e CMCC Research Divisions. In this talk\, we will showcase different Use C
 ases\, related to sectors such as climate change and wildfire management\,
  that demonstrate how the system has been used at CMCC\, within different 
 projects and initiatives\, for building downstream products and services t
 hat need to access\, analyse and process Earth Science data.
DTSTAMP:20260415T003046Z
LOCATION:Poster presentation
SUMMARY:A Cloud-native Data Platform for Management and Processing of Big G
 eospatial Data - Monia Santini\, Melissa	Latella
URL:https://pretalx.earthmonitor.org/gw2023/talk/73NPHA/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-BT7JNX@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T104500
DTEND;TZID=Europe/London:20231005T105000
DESCRIPTION:The establishment of the European Open Science Cloud (EOSC) is 
 one of the eight priorities of the European Open Science Agenda (2018)\, w
 ith the ambition of enabling to federate multidisciplinary research infras
 tructures.  Among ‘Enabling an operational\, open and FAIR EOSC ecosyste
 m (INFRAEOSC)’ projects contributing to this priority\,  Blue-Cloud 2026
  and AquaINFRA have been funded to protect oceans\, seas\, coastal and inl
 and waters\, in contribution to achieve the goals of the EU Mission "Resto
 re our Ocean and Waters” by 2030.  \n\nFurthermore\, the European data s
 trategy (2020) identifies data spaces as the instruments to achieve a sing
 le market for data across sectors and countries\, through a common and int
 eroperable framework.  In particular\, the Green Deal Data Space will be i
 nterlinked with the EOSC ecosystem demonstrated by the Blue-Cloud 2026 and
  AquaINFRA projects\, involving several research communities and data infr
 astructures that are contributing to enabling the European Digital Twin of
  the Ocean.\n \nThe main objective of the AquaINFRA project is to develop 
 a research infrastructure equipped with FAIR multi-disciplinary data and s
 ervices\, allowing seamless data discovery and processing through an AquaI
 NFRA Interaction Platform (AIP) in order to support marine and freshwater 
 scientists and stakeholders and interact with EOSC seamlessly. More specif
 ically\, the AIP will include developing a cross-domain and cross-country 
 search and discovery mechanism as well as building services for spatio-tem
 poral analysis and modelling through Virtual Research Environments (VREs) 
 where regional case studies are demonstrated highlighting the Mediterranea
 n use cases in this presentation.
DTSTAMP:20260415T003046Z
LOCATION:Poster presentation
SUMMARY:AquaINFRA for blue research interlinking EOSC ecosystem with Green 
 Deal data space - Kaori Otsu and Imma Serra
URL:https://pretalx.earthmonitor.org/gw2023/talk/BT7JNX/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-A3WTGT@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T105000
DTEND;TZID=Europe/London:20231005T105500
DESCRIPTION:The Knowledge Centre on Earth Observation activity is grounded 
 in sound knowledge management practices and cutting-edge NLP technologies.
  It aims to create a common scaffolding for research projects on the one h
 and and policy needs on the other. \n\n \nThe User Requirement Database (U
 RDB) stores and validates Core Copernicus Users requirements for Earth Obs
 ervation (EO) products and applications. The URDB facilitates automated ga
 p analysis and screenings across diverse data spaces in pursuit of the opt
 imal matching pre-existing solution\, initially examining Copernicus Servi
 ces product catalogues\, followed by a subsequent exploration of research 
 findings from the EU Horizon programme. The Text Mining Application (TMA) 
 leverages innovative advancements in machine learning utilizing Transforme
 rs to facilitate precise semantic document retrieval within an EO-specific
  subset of research outcomes financially supported by the European Union's
  research and innovation framework programmes. These programmes and the re
 spective EO project data span from FP1 in 1984 to the most current initiat
 ive\, Horizon Europe. The primary TMA objective is to empower users with r
 apid access to research findings for highly specific queries\, while simul
 taneously offering a user-friendly database\, an internal microservice as 
 an API\, and a GUI interface for more advanced metrics and visualizations.
  In the future\, the URDB and TMA will be closely interlinked and integrat
 ed\, enabling users of either platform to benefit from rapid access to the
  actual Copernicus datasets\, as well as enhanced meta-information metrics
  and insight into research outcomes. \n\n \nIn addition to supporting gap 
 and fit for purpose analysis\, the main scope of the URDB is to enable req
 uirement retracing across the components of the EO value chain\, from poli
 cy needs to observations\, and therefore supporting and tracking the evolu
 tion of the Copernicus Programme. The URDB's records are technology-agnost
 ic quantitative requirements\, expressed by verifiable\, unambiguous and a
 ctionable technical specifications (horizontal resolution\, measurement un
 certainty\, tasking time\, etc.). The URDB's data model builds on the expe
 rience of existing requirement databases from Copernicus Core Services and
  international partners (e.g.\, USGS and NASA). One of the URDB’s and TM
 A’s core design principles is semantic interoperability: entities\, rela
 tionships and attributes are clearly defined in a terminology and\, when a
 pplicable\, they follow international standards (ISO\, OGC)\, recommendati
 on and best practices (CEOS\, GEO).  \nFrom a technical perspective\, both
 \, the URDB and TMA are self-hosted open-source databases with a GUI and a
 n application layer for querying and performing analysis. While the URDB i
 s based on PostgreSQL\, the TMA utilizes a novel vector database known as 
 Qdrant\, which fulfils highly specific AI requirements and offers a user-f
 riendly API.  \nThe vision for both databases is to link them through web 
 APIs to online data catalogues and tightly integrate them into the existin
 g (meta-) data Copernicus landscape.
DTSTAMP:20260415T003046Z
LOCATION:Poster presentation
SUMMARY:Advancing Earth Observation knowledge management through machine le
 arning and semantic interoperability for EU policy support - Dominik Weckm
 üller\, Iacopo Ferrario
URL:https://pretalx.earthmonitor.org/gw2023/talk/A3WTGT/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-RKDT8Q@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T105500
DTEND;TZID=Europe/London:20231005T110000
DESCRIPTION:In-situ data collection is a fundamental part in the domain of 
 ecosystem observations and monitoring. Continuous measurements of energy a
 nd matter exchanges at the ecosystems/atmosphere boundary by means of the 
 eddy covariance (EC) technique are fundamental observations across the wid
 e range of in-situ measurements\, in particular concerning carbon and othe
 r greenhouse gases and water balances. Monitoring stations based on this t
 echnique and organised in networks at different scales\, from national to 
 global\, providing precious insights to different users\, are a standard i
 n the environmental sector. The Integrated Carbon Observation System (ICOS
 ) is one of such research infrastructures\, working at the European scale.
  The ecosystem domain of ICOS\, dealing with terrestrial observations in n
 atural and anthropic ecosystems\, is not only providing EC datasets\, but 
 also numerous meteorological and other auxiliary variables to support the 
 activity of the network. In the present work we describe the portfolio of 
 the main products included in a typical ICOS ecosystem station: from conti
 nuous measurements of CO2 and H2O exchanges\, to the above- and below- gro
 und meteorological parameters\, to the discontinuous ancillary measurement
 s of different vegetation characteristics – spanning from tree height to
  above-ground biomass\, from soil characteristics to plant area index\, fr
 om species distribution to litter mass. The continuous datasets are provid
 ed at different scales\, from half-hourly to yearly. All the datasets\, su
 pplemented by a detailed set of metadata ensuring the consistency with the
  FAIR principles (Findability\, Accessibility\, Interoperability\, Reusabi
 lity)\, are stored on a safe repository and both openly and freely distrib
 uted (with license CC-BY 4.0) to researchers\, modelers\, and any user tha
 t requests them.
DTSTAMP:20260415T003046Z
LOCATION:Poster presentation
SUMMARY:The ICOS ecosystem in-situ measurements portfolio - Simone Sabbatin
 i
URL:https://pretalx.earthmonitor.org/gw2023/talk/RKDT8Q/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-KCZ3UG@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T111500
DTEND;TZID=Europe/London:20231005T113500
DESCRIPTION:Heat waves are more and more heavily affecting population and t
 his is even more enhanced in cities rather than in the countryside where t
 he urban heat island (UHI) effect worsen their duration and intensity. Cur
 rent research on the estimation of the UHI effect adopts 3 main approaches
 : i) observational studies describing its driving processes\, spatial patt
 erns and/or magnitude\; ii) Earth observation (EO) studies focusing on the
  surface urban heat island (SUHI) determined from land surface temperature
  (LST) retrieved from satellite thermal sensors (e.g. Landsat-TIRS\, Senti
 nel-SLSTR)\; iii) modeling via mesoscale meteorological models like the We
 ather Research and Forecasting (WRF) or microscale models (i.e. ENVI-met) 
 that require large computational effort and/or fine tuning of parameters. 
 Municipalities need actionable data to support their decisions. It is thus
  crucial to develop intermediate approaches for the estimation of the UHI 
 intensity and spatial extension without the need of advanced expertise to 
 tune parameters or run complex meteorological models but\, at the same tim
 e\, able to provide reliable insight into the urban air temperature. The w
 ork presented in this contribution is performed in the framework of the US
 AGE project activities and focuses on providing a pipeline for the develop
 ment of UHI maps in urban areas utilizing open data like EO\, IoT ground s
 ensor data\, surface properties and a hybrid model based on machine learni
 ng and geostatistics. We present a pipeline that can be deployed with mino
 r adaptation (i.e STAC end points) within GIS software environments. The g
 round sensor data are accessed via OGC SensorThings API and fed into the a
 nalysis. Pre-loaded 'semi-static' layers\, like DTM\, DSM\, LU/LC\, vegeta
 tion fraction\, urban building morphology and shade maps are accessed via 
 OGC Feature API and utilized to spatialize the air temperature at each tim
 e stamp received from the IoT sensors. Based on the revisit time of EO the
 rmal data and its cloud-coverage level\, LST observations are integrated t
 o help the spatialization of the ground sensor's temperature\, performed u
 sing a hybrid model combining machine learning and geostatistics. This all
 ows for faster computation compared to classical geostatistics but\, at th
 e same time\, to explicitly handle spatial correlation of data and errors.
  The aforementioned pipeline is suitable to derive UHI maps from given IoT
  ground sensor data. On the other end\, to forecast the UHI effect up to 4
 8 h\, the pipeline ingest 2-m air temperature\, relative humidity as well 
 as wind speed and direction from the meteorological models (WRF or AROME) 
 the open-meteo API. \nThe proposed pipeline is applied in the Alpine valle
 y and city of Trento (Italy) and is then validated against high-resolution
  simulations with the WRF model\, offline coupled with an urban parameteri
 zation scheme to reach a resolution of 100 m. The proposed pipeline can be
  used not only as a forecasting tool\, but also as a UHI mitigation and pl
 anning tool by changing the ‘semi-static’ layers that involve the stud
 y area. This allows municipalities to predict the effects of their decisio
 ns.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Estimation of urban heat island integrating IoT sensors and EO ther
 mal open data - Raniero Beber
URL:https://pretalx.earthmonitor.org/gw2023/talk/KCZ3UG/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-SWJG3T@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T111500
DTEND;TZID=Europe/London:20231005T124500
DESCRIPTION:Raster data cube is a four-dimensional array with dimensions x 
 (longitude / easting)\, y (latitude /northing)\, time\, and bands sharing 
 a: (1) single spatial reference system\, (2) constant spatial cell size\, 
 (3) constant temporal duration\, (4) temporal reference defined by a simpl
 e start and end date / time\, resulting in a single attribute value for ev
 ery dimension combination. Building a data cube consists basically in conv
 erting raw irregular raster data into a regular and dense structure\, whic
 h may include information loss and needs to consider user definitions and 
 application restrictions.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Conference Hall
SUMMARY:Build and visualize your own raster data cube - Leandro Parente\, M
 urat Sahin
URL:https://pretalx.earthmonitor.org/gw2023/talk/SWJG3T/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-GMPM3A@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T111500
DTEND;TZID=Europe/London:20231005T120000
DESCRIPTION:Earth Observation (EO) applications enable decision-makers\, re
 searchers\, and specialists to understand the phenomena of our planet\, al
 lowing global changes to be made from local actions taken by the public an
 d private sectors. With the dissemination and use of Open Data practices\,
  the EO applications have been enhanced\, allowing numerous works to be de
 veloped\, ranging from the analysis of anthropic actions on inland waters 
 to the temporal analysis of land use and land cover changes. These advance
 s and improvements in EO applications have made their development complex\
 , requiring several materials to be used together with the data to compose
  the results. Consequently\, organizing\, sharing\, and preserving these a
 pplications and the knowledge within them to enable reproduction and repli
 cation has become a challenge. Often these activities require specific exp
 ertise from researchers and specialists and technical infrastructure.  \n\
 nThe Group on Earth Observations (GEO) and its community promote Open Data
  practices\, being responsible for defining guidelines and developing the 
 Global Earth Observation System of Systems (GEOSS) that enhances access to
  EO data. Recently\, GEO started the development of a new component of the
  GEOSS ecosystem\, the GEO Knowledge Hub (GKH)\, to foster the reproductio
 n and replication of EO applications. Created based on the GEO Data Sharin
 g Principles and the GEO Data Management Principles\, the GKH allows users
  to share their EO applications and the underlying resources (e.g.\, proce
 ssing scripts\, datasets\, and description notes)\, enabling people to und
 erstand\, reproduce and replicate the shared EO application. In the GKH\, 
 the resources of an application can have files (e.g.\, satellite imagery d
 atasets\, in-situ data files) and metadata (e.g.\, title\, authors\, spati
 al location). Furthermore\, each resource can be associated with an indivi
 dual persistent identifier (DOI) created by the GKH\, enhancing disseminat
 ion and citation.  \n\nApplications shared on the GKH can be found and use
 d\, making their knowledge accessible. For this\, the GKH provides high-le
 vel features for organizing the application materials and facilitating the
 ir sharing. In addition\, the GKH has a powerful search engine that enable
 s textual\, thematic (e.g.\, Sustainable Development Goal-oriented search)
 \, and spatial-temporal searches. In addition to share and search capabili
 ties\, the GKH provides features that facilitate community engagement\, su
 ch as discussion sections (Real-time Q&A) and a feedback system. All these
  features are accessible through high-level web interfaces and Rest APIs\,
  allowing the integration of various tools to use the digital repository. 
  \n\nThe GKH is already being used to share and preserve many EO applicati
 ons. For example\, several GEO Work Programme Activities store their appli
 cations in the GKH (e.g.\, GEOGLAM\, GEOVENER\, Digital Earth Africa\, and
  many others). These and other use cases have shown positive results\, ind
 icating that the GKH can assist in organizing\, sharing\, and preserving t
 he knowledge generated in EO applications. Therefore\, in this workshop\, 
 we will introduce the main concepts of the GKH\, guidelines\, and practice
 s in how users can use it to share and preserve EO applications.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Seminar room 2 & 3
SUMMARY:GEO Knowledge Hub to preserve and share EO applications: Introducti
 on and practice - Felipe Carlos
URL:https://pretalx.earthmonitor.org/gw2023/talk/GMPM3A/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-ZPWGPF@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T114500
DTEND;TZID=Europe/London:20231005T120500
DESCRIPTION:This is the story of 2 twin projects (namely AIR-BREAK and USAG
 E) undertaken by Deda Next on dynamic sensor-based data\, from self-built 
 air quality stations to the implementation of OGC standard compliant clien
 t solution.\nIn the first half of 2022\, within AIR-BREAK project (https:/
 /www.uia-initiative.eu/en/uia-cities/ferrara)\, we involved 10 local high 
 schools to self-build 40 low-cost stations (ca. 200€ each\, with off-the
 -shelf sensors and electronic equipment) for measuring air quality (PM10\,
  PM2.5\, CO2) and climate (temperature\, humidity). \nAfter completing the
  assembling\, the stations were provided to high schools\, private househo
 lds\, private companies and local associations. Measurements are collected
  every 20 seconds and pushed to RMAP server (Rete Monitoraggio Ambientale 
 Partecipativo = Partecipatory Environmental Monitoring Network - https://r
 map.cc/).\nHourly average values are then ingested with Apache NiFi into t
 he OGC’s SensorThings API (aka STA) compliant server of the Municipality
  of Ferrara (https://iot.comune.fe.it/FROST-Server/v1.1/) based on the ope
 n source FROST solution by Fraunhofer Institute (https://github.com/Fraunh
 oferIOSB/FROST-Server). STA provides an open\, geospatial-enabled and unif
 ied way to interconnect Internet of Things (IoT) devices\, data and applic
 ations over the Web (https://www.ogc.org/standard/sensorthings/). \nIn sec
 ond half of 2022\, within USAGE project (https://www.usage-project.eu/)\, 
 we released the v1 of a QGIS plugin for STA protocol.\nThe plugin enables 
 QGIS to access dynamic data from heterogeneous domains and different senso
 r/IoT platforms\, using the same standard data model and API. Among others
 \, dynamic data collected by the Municipality of Ferrara will be CC-BY lic
 ensed and made accessible from municipal open data portal (https://dati.co
 mune.fe.it/).
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:From low-cost AirQuality stations to open standard (OGC SensorThing
 s) and open data with open source solutions (FROST + QGIS plugin for senso
 rs) - Luca Giovannini
URL:https://pretalx.earthmonitor.org/gw2023/talk/ZPWGPF/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-7GXJZ7@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T120000
DTEND;TZID=Europe/London:20231005T124500
DESCRIPTION:The Green Deal Data Space (GDDS) will interconnect current frag
 mented and dispersed data from various ecosystems\, both from the private 
 and public sectors to facilitate evidence-based decisions and expand the c
 apacity to understand and tackle environmental challenges\, for example\, 
 for monitoring and reaching environmental objectives in biodiversity\, res
 ilience to climate change\, circular economy and zero pollution strategies
 . \n\nThis workshop will be partaken by projects EuroGEO Action Group for 
 the Green Deal Data Space in their quest to push the boundaries of data pr
 ovision\, and ensure a FAIR and TRUSTworthy data  is available for buildin
 g a more sustainable future. Some outcomes of the workshop may contribute 
 to the new adhoc ISO TC211 working group on data spaces. Within this Works
 hop\, the following projects will present their current approaches towards
  enabling the GDDS:\n\nAD4GD:  The aim is Integrate standard data sources 
 (e.g. Insitu\, RS\, CitSci\, IoT\, AI) in the GDDS\, improve semantic inte
 rperability\, and demonstrate with concrete examples that climate change z
 ero pollution\, biodiversity general problems can be solved.\n\nFAIRiCUBE:
  The core objective is to enable players from beyond classic Earth Observa
 tion domains to provide\, access\, process\, and share gridded data and al
 gorithms in a FAIR and TRUSTable manner.  We are creating the FAIRiCUBE HU
 B\, a crosscutting platform and framework for data ingestion\, provision\,
  analysis\, processing\, and dissemination\, to unleash the potential of e
 nvironmental\, biodiversity and climate data through dedicated European da
 ta spaces.\n\n\nUSAGE (Urban Data Space for Green Deal) will provide solut
 ions for making city-level data (Earth Observation\, Internet of Things\, 
 authoritative and crowdsourced data) available\, based on FAIR principles:
  innovative governance mechanisms\, standard-based structures and services
 \, AI-based tools\, semantics-based solutions\, and data analytics. It wil
 l provide decision makers with effective\, interoperable tools to address 
 environmental and climate changes-related challenges. \n\nB³ - Global bio
 diversity is changing under multiple pressures including climate change\, 
 invasive species and land-use change. Yet biodiversity data are complex an
 d heterogeneous\, making it difficult to understand what is happening fast
  enough for decision makers to react with evidence-based policies. To solv
 e this B³ will create Open workflows in a cloud computing environment to 
 rapidly and repeatedly generate policy relevant indicators and models of b
 iodiversity change.\n\nGREAT: Funded by the Digital Europe program\, aims 
 to establish the Green Deal Data Space Foundation and its Community of Pra
 ctice which builds on both the European Green Deal and the EU’s Strategy
  for Data. The project will deliver a roadmap for implementing and deployi
 ng the Green Deal Data Space\, an infrastructure that will allow data prov
 iders and initiatives to openly share their data to tackle climate change 
 in a multidisciplinary manner.\n\nThe Open Earth Monitor Consortium is wor
 king to contribute infrastructure to the GDDS.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Seminar room 2 & 3
SUMMARY:Let's co-create the Green Deal Data Space - Joan Maso
URL:https://pretalx.earthmonitor.org/gw2023/talk/7GXJZ7/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-JKJXUF@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T121500
DTEND;TZID=Europe/London:20231005T123500
DESCRIPTION:Some remote sensing signals provide valuable information only a
 t spatial scales that are too coarse for comfort for various applications.
  Examples include sun-induced chlorophyll fluorescence (SIF)\, whose signa
 l is related to gross primary productivity (GPP) of vegetation and how thi
 s is impacted by stress\, or vegetation optical depth (VOD)\, which is rel
 ated to how water content is distributed within a canopy\, which itself is
  informative on forest biomass and structure. Within OEMC WP6\, we are dev
 eloping an EO spatial downscaling framework that will be specifically tail
 ored towards improving carbon flux estimations. The spatial downscaling wi
 ll employ finer resolution EO variables to achieve this super-resolution\,
  but this will not be done merely numerically. It will instead combine our
  process-based knowledge of how these variables relate to each other to de
 velop a hybrid modelling approach with knowledge-guided AI. This will furt
 her be implemented using a moving window adaptative approach over a spheri
 cal grid\, which will be made possible by novel developments from OEMC WP3
 . The use case within OEMC is to develop SIF-based 1km spatial resolution 
 GPP flux estimations based on measurements from the TROPOMI sensor on Sent
 inel-5P\, which has a spatial resolution that is larger than 5 km. The mai
 n stakeholder for this product will be the Global Carbon Project (GCP)\, a
 nd in particular RECCAP\, which aims to establish the greenhouse gas (GHG)
  budgets of large regions covering the entire globe at the scale of contin
 ents. Such endeavor would greatly benefit from the fine-level spatialized 
 GPP fluxes we aim to provide.  This use-case will largely leverage on in-s
 itu data provided in OEMC WP4 for validation and calibration\, particularl
 y after specific developments to optimize the matching between in-situ poi
 nts and remote sensing grids is achieved. This presentation will detail th
 e blueprint for this task\, which will combine synergistic efforts across 
 various elements within the OEMC project.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Towards a better estimation of GPP for greenhouse gas accounting th
 rough the combined use of SIF\, spherical grids and knowledge-guided AI - 
 Gregory Duveiller
URL:https://pretalx.earthmonitor.org/gw2023/talk/JKJXUF/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-EYEJB3@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T134500
DTEND;TZID=Europe/London:20231005T140500
DESCRIPTION:While land is increasingly degrading\, robust monitoring approa
 ches are required to identify land degradation processes and to ultimately
  tackle those. Land degradation is commonly assessed by comparison with th
 e immediate past or surrounding. Comparison with its natural potential\, h
 owever\, i.e. a state of minimal human impact\, could give further insight
 s into the full degree of degradation\, accounting for the “shifting bas
 eline syndrome”\, the gradual change in perception of what is considered
  the reference. Primary production is one of the key indicators for determ
 ining impacts of land degradation\, which can be approximated by FAPAR\, t
 he fraction of absorbed photosynthetic active radiation\, a metric directl
 y related to primary productivity. Here\, we present a novel methodology t
 o assess land degradation in reference to its natural potential. Using a m
 achine learning model approach\, global time series maps spanning 2000 - 2
 022+ will be generated by simulating potential natural FAPAR in the hypoth
 etical space of minimal human impact. This will allow performing gap analy
 ses of actual and potential natural FAPAR to monitor impacts of land degra
 dation and restoration efforts through time. Use-case scenarios on country
  level and project investment level will be demonstrated in the context of
  supporting UNCCD targets for land degradation neutrality (LDN). This rese
 arch is carried out within the Open Earth Monitor Cyberinfrastructure proj
 ect (OEMC) and received funding via the European Union's Horizon Europe pr
 ogramme under grant agreement No.101059548.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Mapping land potential and tracking land degradation using EO data 
 - Julia Hackländer
URL:https://pretalx.earthmonitor.org/gw2023/talk/EYEJB3/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-ZYJF7M@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T134500
DTEND;TZID=Europe/London:20231005T151500
DESCRIPTION:openEO develops an open API to connect R\, Python\, JavaScript 
 and other clients to big Earth observation cloud back-ends in a simple and
  unified way.\nopenEO Platform implements the openEO API in an federated c
 loud platform. Hence\, it allows to process a wide variety of earth observ
 ation datasets in the cloud. \nUsers interact with the API through clients
 . This demonstration shows the usage and capabilities of the main clients:
  The Web Editor\, the Python Client and the R-Client.\nThe Web Editor is a
  webtool to interactively build processing chains by connecting the openEO
  processes visually. This is the most intuitive way to get in touch with o
 penEO Platform. \nThe Python Client and the R-Client are the openEO Platfo
 rm entry point for programmers. The are available via Comprehensive R Arch
 ive Network (CRAN). They facilitate the interaction with the openEO API wi
 thin the respective programming languages and integrate the advantages of 
 the available geospatial packages and typical IDEs.\nThe classroom trainin
 g teaches users how to accomplish their first a round trip through a typic
 al openEO Platform workflow: login to openEO Platform\, data and process d
 iscovery\, process graph building adapted to common use cases\, processing
  of data and the visualization of results\nBy combining the approaches of 
 the visually interactive Web Editor and the programming based clients user
 s are introduced stepwise to the concepts of openEO Platform and will grad
 ually understand the logic behind openEO.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Conference Hall
SUMMARY:OpenEO in action. Learn how to get started with openEO via the Web 
 Editor\, Python and R. - Peter Zellner\, Michele Claus
URL:https://pretalx.earthmonitor.org/gw2023/talk/ZYJF7M/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-CJREWE@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T134500
DTEND;TZID=Europe/London:20231005T143000
DESCRIPTION:Climate change is profoundly affecting the global water cycle\,
  increasing the likelihood and severity of extreme water-related events. B
 etter decision support systems are essential to accurately predict and mon
 itor water-related environmental disasters and to manage water resources o
 ptimally. These will need to integrate advances in remote sensing\, in-sit
 u and citizen observations with high-resolution Earth system modelling\, a
 rtificial intelligence\, information and communication technologies and hi
 gh-performance computing.\nThe Digital Twin of the Earth (DTE) for the wat
 er cycle is a breakthrough solution that provides digital replicas to moni
 tor and simulate Earth processes with unprecedented spatial-temporal resol
 ution and explicitly including the human component into the system. To get
  the target\, advances in observation technology (satellite and in situ) a
 nd modelling are pivotal. The workshop will serve the community to assess 
 the state of the art of these technologies and to identify challenges to b
 e addressed in the near future.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Seminar room 2 & 3
SUMMARY:Building a Digital Twin Earth for the Water Cycle: State of the Art
  and Challenges - Luca Brocca
URL:https://pretalx.earthmonitor.org/gw2023/talk/CJREWE/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-CFDYTF@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T140500
DTEND;TZID=Europe/London:20231005T142500
DESCRIPTION:Extreme weather and outbakes phenomena are occurring more and m
 ore frequently and are affecting forests throughout the Alpine region. Eff
 icient and targeted forest management on a municipal\, provincial or regio
 nal scale requires high quality\, information-rich remote sensing data. Ai
 rborne hyperspectral imagery enables the acquisition of high-resolution da
 ta on entire portions of land in a short space of time\, whereby the high 
 amount of spectral information provides efficient tools for forest manager
 s\, such as mapping forest species\, identifying invasive species\, mappin
 g bark beetle damage\, calculating narrowband vegetation indices and analy
 zing health status. AVT Airborne Sensing Italia (AVT-ASI) uses the Specim 
 AisaFenix sensor for hyperspectral image acquisition. The sensor works in 
 the VNIR and SWIR spectral ranges and acquires 384 bands in pushbroom mode
 . At the beginning of October 2022\, AVT-ASI acquired hyperspectral images
  over a forest area with size 350 km² near Bruneck\, in the South Tyrol p
 rovince\, Italy\, (Figure 1 a and b) to support the local forestry inspect
 orate in the evaluation of the forest health status. Indeed the area of in
 terest has been strongly affected by the spread of the bark beetle\, proba
 bly due to the damages caused by Vaia storm in 2018\, followed by dry peri
 ods. The AisaFENIX images were preprocessed to correct atmospheric\, radio
 metric and geometric effect\, and then the most frequent tree species were
  mapped with machine learning algorithms. For the Picea abies (Norway spru
 ce) class\, further analysis were conducted using multiple narrowband vege
 tation indices (Figure 1 c to h)\, in order to assess the health status of
  the trees (Figure 2) and detect the effects of the presence of the bark b
 eetle. The results have been validated by the forestry inspectorate with g
 round surveys. The georeferenced thematic product obtained by the hyperspe
 ctral aerial images resulted to be very useful for optimal forest manageme
 nt\, in particular for the identification of possible infected trees at an
  early stage (green-attack) and the implementation of mitigation measures.
  The information was available at a degree of accuracy that is not achieva
 ble by VNIR + SWIR images acquired by satellite platforms\, due to the low
  spatial resolution. However the availability of regular and frequent sate
 llite images has the potential to allow for temporal analysis and change m
 onitoring\, starting from the detailed as-is situation obtained from the a
 erial hyperspectral images.\nThe presentation will show the scientific app
 roach of the work done and critically discuss the achieved accuracy in the
  intermediate and final products.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Use of airborne hyperspectral images in support to Alpine forest ma
 nagers - Thomas Maffei
URL:https://pretalx.earthmonitor.org/gw2023/talk/CFDYTF/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-VDLNJB@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T142500
DTEND;TZID=Europe/London:20231005T144500
DESCRIPTION:Estimating crop yields timely\, is pivotal for official statist
 ics on agricultural productivity to inform policy-making on sustainable fo
 od production. Existing approaches of collecting yield data annually from 
 a large number of farms are resource-intense\, though. Official crop yield
  statistics in Germany\, for instance\, relies heavily on extensive and ti
 me-consuming farm surveys and on-farm measurements.\n\nThe EU’s Copernic
 us earth observation (EO) program provides a plethora of satellite data\, 
 enabling the remotely sensed monitoring of agricultural land at high spati
 o-temporal resolution. EO imagery\, open geospatial data on meteorological
  conditions and soil properties as well as advances in machine learning (M
 L) provide huge opportunities for model based crop yield estimation\, cove
 ring large spatial scales with unprecedented granularity. Managing vast am
 ounts of multi-source data required for yield modelling remains a challeng
 e\, though\, particularly for public authorities. We present a model-based
  approach to estimate yields of multiple major crops cultivated in Germany
 \, by employing ML ensembles\, using a cloud-integrated spatial data infra
 structure (SDI). Our SDI is built on interconnected components linking EO 
 cloud computation and data storage\, using the CODE-DE platform\, with int
 ernal data cubes through web services.\n\nOur model-based yield estimation
  approach integrates a number of dynamic and static predictors. Analysis r
 eady data of multi-spectral Sentinel-2 imagery is used for space-borne ret
 rieval of crop traits such as leaf area index and above ground biomass. Ge
 ospatial data on meteorological time-series are queried from our data cube
 \, providing daily variables such as temperature\, precipitation\, and glo
 bal radiation. External geospatial data on soil moisture and physicochemic
 al soil properties are obtained from the Copernicus Global Land Service an
 d SoilGrids 2.0 data portals\, respectively. Crop-specific ML models are t
 rained on multi-annual data (2018 - 2022) collected at agricultural parcel
  level for three crops\, i.e. winter wheat\, winter barley\, and winter ra
 pe. The ensemble of ML regressors employed\, includes Gradient boosted tre
 es (CatBoost\, LightGBM\, XGBoost)\, Partial Least Squares\, RandomForest\
 , and Support Vector Machines. Parcel geometries obtained from the Integra
 ted Administration and Control System (IACS) enable the spatially scaled a
 pplication of trained yield models\, covering larger administrative region
 s represented by two federal states.\n\nRSQ values of best performing mode
 ls\, inferred from cross validations at parcel level\, range between 0.67 
 - 0.74. Related normalized RMSE (nRMSE) values range between 12 - 19%. Agg
 regated yield estimates at district level compared against mean yields at 
 district level obtained from official yield statistics for 2020 and 2021 s
 how RSQ values for best performing models\, ranging between 0.57 - 0.85. R
 elated nRMSE values range between 5 - 10%.\n\nPreliminary results are prom
 ising\, suggesting several advantages compared to traditional yield estima
 tion approaches\, regarding area coverage\, cost effectiveness\, and timel
 iness. Our cloud-integrated SDI used as backbone enables full scalability 
 for crop yield estimation at national scale. However\, high quality traini
 ng data inferred from a representative sampling across the country and ope
 n data access for IACS parcel geometries are required to lift current scal
 ability barriers.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Machine learning ensembles for scalable crop yield modelling: Lever
 aging earth observation and a cloud-integrated spatial data infrastructure
  to support agricultural statistics - Patric Brandt
URL:https://pretalx.earthmonitor.org/gw2023/talk/VDLNJB/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-FSTSBB@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T143000
DTEND;TZID=Europe/London:20231005T151500
DESCRIPTION:In recent years\, several new satellite constellations have bee
 n put into service. This\, together with the new policies for open data di
 stribution\, dramatically increased the availability of time-series with h
 igh temporal resolution.\n\nThe new widespread availability of high tempor
 al resolution imagery has led to paradigm shift from change detection tech
 niques where pairs of images are compared searching for abrupt changes  (e
 .g. forest fires\, forest cuts)\, to methods capable of tracking changes c
 ontinuously in time. In particular\, time-series allows for the monitoring
  of subtle and gradual changes for which the definition of a pre and post 
 event date is not straightforward  (e.g.\, vegetation stress caused by dro
 ught\,  bark beetle outbreaks) and anthropogenic processes happening at a 
 finer timescale (e.g. mowing events). \nSuch data availability\, together 
 with increasing ease of access to both offline computing power and to clou
 d based computing platforms and new tools for data processing\, is leading
  to the development of a wide variety of applications for near real-time m
 onitoring using Earth Observation (EO) data intended to be used in decisio
 n making processes (e.g.\, forest management) by stakeholders such as gove
 rnment agencies. In this context\, we present monitoring tools\, implement
 ed on the Google Earth Engine platform\, that exploit spaceborne EO data t
 o support decision making in Alpine environments affected by two threats c
 onnected to global change: pests outbreaks and land use intensification. \
 nAfter the Vaia storm in 2018\, bark beetle outbreaks have become more fre
 quent in the Alps with estimates\, at the end of 2022\, of 8000 hectares i
 nfested by the pests only in the Trento province. Such phenomena must be m
 onitored by detecting both past and new outbreaks. This is critical for th
 e definition of recovery strategies for the affected areas and mitigation 
 strategies to limit the spread of new outbreaks. The developed tool analyz
 es long Sentinel-2 time-series for bark beetle outbreaks mapping\, generat
 ing a product that identifies the area hit by an attack and the first year
  and month of the detection. By processing new images as they are acquired
 \, it performs a near real-time monitoring highlighting new attacks as soo
 n as they are visible from the satellite data. This tool is currently bein
 g used by the Forest Service of the Province of Trento that is providing t
 he generated products to the local stations.\n\nThe second tool we present
  uses vegetation indices time-series derived from Sentinel-2 imagery to es
 timate grassland mowing frequency. Grasslands in Europe are facing managem
 ent intensification in accessible areas and abandonment in marginal ones\,
  with significant consequences not only for grassland productivity\, but a
 lso for fodder quality\, nitrogen leaching\, animal and plant diversity an
 d grassland recreational value. For these reasons the availability of gras
 sland mowing frequency data can contribute to the development of more targ
 eted conservation and management measures. The model is now being used in 
 several research and management contexts\, including CAP subsidies conditi
 onality monitoring and habitat suitability for ground nesting endangered b
 ird identification.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Seminar room 2 & 3
SUMMARY:Tools for Long Time-Series Processing For Alpine Environments Monit
 oring - Daniele Marinelli\, Davide Andreatta
URL:https://pretalx.earthmonitor.org/gw2023/talk/FSTSBB/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-BAYEKQ@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T144500
DTEND;TZID=Europe/London:20231005T150500
DESCRIPTION:Land cover changes affect the climate system at local\, regiona
 l\, and global scales. Previous studies have indicated that the changes in
  vegetation distribution have an impact on the land surface temperature an
 d the energy balance at local and global scales. Assessing the effect of l
 and cover change on climate variables is a fundamental step in understandi
 ng how deforestation and reforestation processes will impact the climate d
 ynamics. At the same time\, this knowledge can be of utmost importance for
  the design of reforestation or afforestation plans\, such as those envisa
 ged within the European Union’s Green New Deal\, but also more generally
 \, in any part of the world. In this talk\, we will present some prelimina
 ry results on the effect of land cover change on climate variables for Eur
 ope and Africa. To develop the studies\, we will develop a first technical
  implementation of the space for time technique in the programming languag
 e Julia. The space for time technique estimates the average change of loca
 l climate if the land cover changes from one class to another. For example
 \, savannas are usually hotter than neighboring forest\, then contrasting 
 the local climate conditions we can evaluate the potential effect of the t
 ransition from forest to savannas. In the current state of Earth observati
 on using satellite imagery\, downloading large amounts of information is n
 o longer feasible because the amount of information is many times larger t
 han the infrastructure of research institutions and organizations. In this
  context\, the development of software compatible with cloud computing inf
 rastructure is more important than ever. Julia is a dynamic programming la
 nguage focused on high-performance computation and easy scalability\, foll
 owing the philosophy of “write like python\, run like C”. Despite bein
 g a relatively new programming language (11 years old)\, the use of Julia 
 in science and cloud computing has grown exponentially recently\, as more 
 and more institutes and companies adopt it. For these reasons\, our implem
 entation of the space-for-time technique on the Julia programming language
 \, will allow scientists and organizations to efficiently perform the anal
 ysis from laptops to remote servers and platforms.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Towards a large-scale tool for estimating potential land-cover impa
 cts from Remote Sensing - Daniel E. Pabon-Moreno
URL:https://pretalx.earthmonitor.org/gw2023/talk/BAYEKQ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-FZXGJQ@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T152000
DTEND;TZID=Europe/London:20231005T152500
DESCRIPTION:In this talk we introduce a community led initiative part of th
 e wider Open Innovation framework at European Space Agency that works to d
 evelop an open\, interactive\, user intuitive platform for a constantly up
 dated\, comprehensive and detailed overview of the dynamic environment of 
 the open source digital infrastructure for geospatial data storage\, proce
 ssing and visualisation systems. OSS4gEO is designed as a repository that 
 functions as an extended metadata catalogue\, curated by the community and
  a tool for metrics computation\, visualisation\, ecosystem statistical an
 alysis and reporting.\n\nThe initial development of the Open Source for Ge
 ospatial Software Resources platform builds on previous extensive work sta
 rted in 2016 that has materialised into a pioneering overview of open sour
 ce solutions for geospatial\, voluntarily updated by the team. Starting in
  2023\, OSS4gEO has become a part of a wider ESA Earth Observation (EO) Op
 en Innovation initiative to actively support and contribute to the EO and 
 geospatial open source community and it is intended as a seed action to be
 tter understand\, represent and harvest the geospatial open source ecosyst
 em. \n\nThere are 3 main objectives that OSS4gEO aims to achieves: \n(1) I
 t aims to offer an informed and as complete as possible overview of the op
 en source for geospatial and EO ecosystem\, together with various capabili
 ties of filtering and visualisations\, within the platform as well as tech
 nical solutions to programmatically access and extract data from the datab
 ase (APIs) to use in any purpose\, including commercial\;\n(2) It aims to 
 provide guidance through the complexity of the geospatial ecosystem so tha
 t one can choose the best solutions\, while understanding their sustainabi
 lity\, technical and legal interoperability and all the dependencies level
 s\;\n(3) It aims to serve as a community building\, a promoting and mainta
 ining platform for new and innovative open source solutions for EO and geo
 spatial\, developed within various projects\, research centres\, small or 
 large companies\, universities or through individual initiatives.
DTSTAMP:20260415T003046Z
LOCATION:Poster presentation
SUMMARY:OSS4gEO: a FOSS4G resources platform initiative - Codrina Maria Ili
 e\, Vasile Crăciunescu
URL:https://pretalx.earthmonitor.org/gw2023/talk/FZXGJQ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-PYQJXH@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T152500
DTEND;TZID=Europe/London:20231005T153000
DESCRIPTION:In the framework of Earth Observations (EO)\, in-situ measureme
 nts are a fundamental pillar for the characterisation of ecosystem behavio
 ur. Vegetation responses to stressors\, trends and changes in ecological f
 unctioning\, species abundance and characterisation are only a few example
 s of currently available in-situ datasets worldwide. The combination of in
 -situ timeseries with other EO products\, such as remote sensing and other
  geospatial datasets\, gives rise to the possibility of characterising\, m
 odeling and predicting ecosystem functionalities and dynamics from local t
 o global scales. In this framework\, the Horizon-Europe funded project Ope
 n-Earth-Monitor Cyberinfrastructure (OEMC) aims at collecting a wide range
  of such datasets\, elaborating them together with other EO products\, and
  creating specific technological tools to ease their sharing and usability
 . A consistent part of the project is dedicated to gathering and analysing
  a huge in-situ datasets portfolio\, characterized by a large variety in t
 erms of data types\, scales\, accuracy and documentation. In-situ observat
 ions potentially available to the project span from continuous monitoring 
 (e.g. greenhouse gas fluxes) to sampling campaign (e.g. species distributi
 on)\, from half-hourly to yearly scales\, from highly-standardised dataset
 s to citizen science observations\, from remote sensing datacubes to singl
 e tree measurements\, from vegetation to fauna checklists\, from terrestri
 al to freshwater habitats\, and so forth. The need for harmonisation is hu
 ge\, especially concerning the relevant metadata. In the present poster we
  report on the main characteristics of such in-situ datasets\, including t
 he spatial and temporal scales\, accessibility\, format and standardizatio
 n.
DTSTAMP:20260415T003046Z
LOCATION:Poster presentation
SUMMARY:Contribution of in-situ observations to the Open-Earth-Monitor Cybe
 rinfrastructure project - Simone Sabbatini
URL:https://pretalx.earthmonitor.org/gw2023/talk/PYQJXH/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-WPTQMC@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T153000
DTEND;TZID=Europe/London:20231005T153500
DESCRIPTION:Discrete Global Grid Systems (DGGS) tessellate the surface of t
 he earth with hierarchical cells of equal area\, minimizing distortion and
  loading time of large geospatial datasets\, which is crucial in spatial s
 tatistics and building Machine Learning models. Successful applications of
  DGGS include the prediction of flood events by integrating remote sensing
  data sets of different resolutions\, as well as vector data. Here we pres
 ent DGGS.jl: An analysis framework for scalable geospatial analysis writte
 n in the Julia programming language. Bindings from the C++ library DGGRID 
 were created to convert between geographical coordinates and DGGS cell ids
 \, as well as to provide several projections and grids. An efficient data 
 structure and chunking scheme based on data cubes and Zarr-arrays was crea
 ted to store remote sensing data of different resolutions\, structured in 
 accordance with the selected grid. This provides the basis for fast and a
 ccurate ML modeling\, especially distortion-less and spatially aware Graph
  Convolutional Neural Networks. Furthermore\, the hierarchical cell struct
 ure of a DGGS enables multiscale modeling\, in which regions of interest c
 an be represented in a higher resolution than others.
DTSTAMP:20260415T003046Z
LOCATION:Poster presentation
SUMMARY:DGGS.jl: A Julia Package for Scalable Geospatial Analysis Using Dis
 crete Global Grid Systems - Daniel Loos
URL:https://pretalx.earthmonitor.org/gw2023/talk/WPTQMC/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-BYCPAH@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T153500
DTEND;TZID=Europe/London:20231005T154000
DESCRIPTION:The European Commission Knowledge Centre on Earth Observation (
 KCEO) is developing two use cases\, aiming to provide sustained last-mile 
 products and services emerging from the analysis of EU policy needs\, EU F
 ramework Programmes for Research and Innovation and GEO activities.  \n\nT
 he first use case to transfer into a sustained product and service has eme
 rged from the evaluation of research projects in connection to EuroGEO. In
  this respect\, the EuroGEO Showcases: Applications Powered by Europe (e-s
 hape) project\, having received funding from the H2020 programme and aimin
 g to ensure the optimal implementation of EuroGEO is the primary project t
 o evaluate. The pilots of the e-shape project were the candidate use cases
  considered for implementation and further development by KCEO. They have 
 been evaluated by criteria that consider policy relevance and technical as
 pects\, such as data sources and infrastructures and European principles r
 elated to these. As a result of the evaluation\, KCEO selected the photovo
 ltaic energy assessment at an urban scale pilot led by ARMINES as the firs
 t adopted project. Through the implementation of use cases in a prototypic
 al EuroGEOSS virtual ecosystem\, it will also be possible to define the go
 od practices and technologies to be used in the future\, operational EuroG
 EOSS more thoroughly. The use case will be shaped according to the needs o
 f the policy Directorate-Generals (DGs) through the KCEO Deep Dive on Clim
 ate Change Adaptation in Urban Areas in collaboration with ARMINES.  \n\nT
 he second use case focuses on monitoring wetlands’ change and degradatio
 n processes across EU Natura 2000 (N2K) sites. The use case has been devel
 oped from the identification of DG Environment (ENV) policy needs at the p
 olicy implementation and evaluation stages of the Habitats Directive and i
 mplements the results of the chapter on wetlands of the KCEO Deep Dive on 
 Biodiversity assessment (upcoming science for policy report in 2023). The 
 goal of the use case is to cover the last mile of the EO value chain to en
 able the full exploitation of EO products and derived products and to fost
 er their uptake in policy making. This should result from 1) requirements 
 translation and co-development with DG ENV stakeholders\, 2) fitness for p
 urpose analysis of existing products and applications\, 3) co-design of th
 e web application information content\, graphics and features\, 4) identif
 ication of gaps and recommendations for the improvement of products and 5)
  provision of a working prototype as a proof of concept for an operational
  service. The project will be framed around three spatial scales of intere
 st: pan-European\, river basin and N2K site and four application services:
  habitat mapping\, pressure and condition trend analysis\, pressure and co
 ndition monitoring and hotspot analysis. The use case acknowledges the imp
 ortance of integrating EO data from multiple sensors with other data\, inc
 luding hydrological modelling outputs. \n\nThe adoption and development of
  these use cases will answer the specific needs of the EU policies\, incre
 ase the use of Copernicus data and services and provide visibility to the 
 projects identified generating more value from the investments already mad
 e.
DTSTAMP:20260415T003046Z
LOCATION:Poster presentation
SUMMARY:EO-based Use Cases for EU Policy Support - Ilaria Gliottone\, Canda
 n Eylül Kilsedar\, Iacopo Ferrario
URL:https://pretalx.earthmonitor.org/gw2023/talk/BYCPAH/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-GVW3U8@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T160000
DTEND;TZID=Europe/London:20231005T162000
DESCRIPTION:In the context of global change\, the biosphere has been experi
 encing systematic changes. Biospheric changes are not only linked to the a
 tmosphere via the variability and changes of climate but also linked to so
 cio-economic drivers. Socio-economic and climate drivers may both act eith
 er slowly\, causing trends\, or abruptly\, causing extreme events\, shocks
 \, or tipping points. However\, the relative importance of climate and soc
 io-economic factors for the biosphere dynamics and their moderating mechan
 isms is not well understood and may vary spatially. To gain insights into 
 the links between climate\, biosphere\, and society\, we study the relatio
 nships between the trajectories in the three different domains by analyzin
 g multi-stream global data from 2001-2020\, including a biospheric and cli
 mate data cube and subnational socio-economic data. We hypothesize that cl
 imatic change is a globally widespread driver\, almost relevant everywhere
 \, but can be additionally mediated by socio-economic drivers (e.g. land-u
 se and freshwater management). Another hypothesis is that social-economic 
 shocks can lead to an unsystematic shift of biospheric resilience after cl
 imate extremes. This study aims to quantify biospheric responses to climat
 e and society from data-driven signals\, and has an essential implication 
 on understanding human footprints on biospheric changes globally.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Integrated data-driven analysis of climate\, biosphere and socio-ec
 onomic co-variability: towards a planetary health index - Wantong Li
URL:https://pretalx.earthmonitor.org/gw2023/talk/GVW3U8/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-BHQ9JG@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T160000
DTEND;TZID=Europe/London:20231005T173000
DESCRIPTION:this workshop focuses on modern architectures built around clou
 d computing intended for the processing of EO data and facilitation of rel
 evant applications. among the tools discussed are machine learning enabled
  modules that support data classification\, annotation and compression. Su
 ch tools combined with data fusion and semantic information processing tra
 nsform EO primitive data into meaning rich data sets that directly match a
 pplication (vertical) requirements. this suite of tools is analytically pr
 esented and discussed in details throughout the workshop.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Conference Hall
SUMMARY:Machine Learning tools and systems support for EO data processing a
 nd applications - Stathes Hadjiefthymiades
URL:https://pretalx.earthmonitor.org/gw2023/talk/BHQ9JG/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-3SF9JG@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T160000
DTEND;TZID=Europe/London:20231005T173000
DESCRIPTION:OpenLandMap is a not-for-profit open data system providing data
  and services to help produce and share the most up-to-date\, fully docume
 nted (potentially to the level of fully reproducibility) data sets on the 
 actual and potential status of multiple environmental variables. The layer
 s include soil properties/classes\, relief\, geology\, land cover/use/degr
 adation\, climate\, current and potential vegetation\, through a simple we
 b-mapping interface allowing for interactive queries and overlays. This is
  a genuine Open Land Data and Services system where anyone can contribute 
 and share global maps and make them accessible to hundreds of thousands of
  researchers and businesses. We currently host about 15TB of data includin
 g 1 km daily and monthly climatic products (min\, max temperature and prec
 ipitation)\, map of potential natural vegetation\, 250 m MODIS terra produ
 cts\, 100 m land cover and land use maps and soil properties\, 30 m land c
 over maps and digital terrain parameters. We are inspired by de-centralize
 d open source projects such as Mastodon\, OpenStreetMap\, and OSGeo projec
 ts including the R project for statistical computing.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Seminar room 2 & 3
SUMMARY:OpenLandMap: Global ARCO environmental layers - Tom Hengl (OpenGeoH
 ub)
URL:https://pretalx.earthmonitor.org/gw2023/talk/3SF9JG/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-83UWBF@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T163000
DTEND;TZID=Europe/London:20231005T165000
DESCRIPTION:Urban land use and surface properties play a major role in dete
 rmining the quality of life for citizens and for urban planning. They have
  a strong impact alongside extreme events and phenomena\, such as flash fl
 ooding due to heavy rains on a highly impermeabilized city\, Urban Heat Is
 land (UHI) intensification during heat waves\, biodiversity reduction in g
 reen areas\, etc.\nTo allow the study of such phenomena\, high-resolution 
 aerial and terrestrial data acquired with different sensors can be merged 
 and processed with Machine Learning (ML) approaches in order to describe a
 nd predict the state of urban landscapes and create data-driven actionable
  insights.\nWithin the USAGE - Urban Data Space for Green Deal - project [
 https://www.usage-project.eu/]\, this work investigates the integration of
  multi-source data in urban areas for environmental analyses. Two pilot ci
 ties are considered\, Graz (Austria) and Ferrara (Italy)\, where multispec
 tral\, thermal\, hyperspectral and LiDAR data were acquired from aerial fl
 ights. \nFirstly\, all multi-modal data were processed and co-registered i
 n order to align them. Then\, the proposed workflow uses aerial hyperspect
 ral images to classify the surface material with ML algorithms (16-18 clas
 ses\, normally)\, thanks to the availability of spectral information in th
 e VNIR and SWIR ranges. The material properties are used to support the ca
 lculation of land surface temperatures (LST) from the aerial thermal image
 s acquired in the LWIR range. The operation is critical in case of special
  materials\, like metals\, that originate false temperature values in the 
 thermal images\, given their low emissivity. As ground truth for the LST e
 stimation\, a series of ground measurements are performed during the therm
 al flights. The comparison of the temperatures measured on the ground and 
 from the thermal camera underlines the influence of the atmosphere\, and t
 herefore the need of rigorous modeling for the correction of atmospheric a
 bsorption and scattering. The LST values derived from aerial thermal image
 s are compared to those retrieved from Landsat TIRS images\, in order to c
 haracterize the representativeness of the Landsat pixel over the urban lan
 dscape. Finally\, the LiDAR point clouds can be enriched with the outcomes
  of the thermal and hyperspectral analyses for a more realistic and exploi
 table visualization of the territory.\nThe proposed workflow has been test
 ed first on the data acquired on the city of Graz in 2021\, then replicate
 d and validated on the data acquired on the city of Ferrara in 2022. The p
 roposed methodology could be replicated also in other similar cities to ga
 in more insight from LST retrieved from Earth Observation data that are le
 ss resolute in space (~ 70 m) but guarantee higher revisit time\, thus all
 owing for monitoring the evolution of the land cover within the urban envi
 ronment.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Airborne-based multi-modal geospatial data for local Green Deal - R
 aniero Beber
URL:https://pretalx.earthmonitor.org/gw2023/talk/83UWBF/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-ACHCUS@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T170000
DTEND;TZID=Europe/London:20231005T172000
DESCRIPTION:The System of Environmental Economic Accounting has been develo
 ped by the global statistical committee under auspices of the United Natio
 ns Statistical Division. It comprises two complementary parts: the Central
  Framework and Ecosystem Accounting\, that jointly allow recording and ana
 lysing environmental data\, and connecting environmental information to ec
 onomic activities including indicators such as GDP. The SEEA uses a suite 
 of connected physical and monetary indicators that are included in spatial
  datasets as well as accounting tables. This presentation will introduce t
 he audience to the SEEA\, show how spatial data (mainly from Earth Observa
 tion) is used in SEEA\, and demonstrate how SEEA can be used to bring spat
 ial data to a wide range of users. It will also link the SEEA to various E
 U policy initiatives including the Green Deal.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:The System of Environmental Economic Accounting: connecting spatial
  data and users - Arnan Araza
URL:https://pretalx.earthmonitor.org/gw2023/talk/ACHCUS/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-H37GJM@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T090000
DTEND;TZID=Europe/London:20231006T093000
DESCRIPTION:This talk will discuss how Brazil's National Institute for Spac
 e Research (INPE) is transitioning from visual interpretation to automatic
  classification of its Amazon deforestation monitoring system.  The presen
 tation will discuss the methods for machine learning using time series of 
 Sentinel-2/2A images that have managed to reach the same accuracy as remot
 e sensing experts.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Monitoring deforestation in Amazonia: transitioning from visual int
 erpretation to satellite time series analysis - Gilberto Camara
URL:https://pretalx.earthmonitor.org/gw2023/talk/H37GJM/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-8M7MXV@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T093000
DTEND;TZID=Europe/London:20231006T100000
DESCRIPTION:Global land use and land cover monitoring is crucial for unders
 tanding and addressing the impacts of land use change on the environment a
 nd society. The World Resources Institute’s Land and Carbon Lab is dedic
 ated to advancing this field through the development of cutting-edge monit
 oring tools\, technologies\, and partnerships. In this presentation\, we w
 ill showcase and review available products for global land use and land co
 ver monitoring and highlight the ways in which these products are being le
 veraged to drive positive change. We will explore the challenges of aligni
 ng Earth Observation data with policy\, including spatial and temporal cha
 llenges as well as challenges aligning land use definitions to land cover 
 monitoring products. The presentation will conclude with a discussion of t
 he future directions for this exciting field\, and the opportunities to en
 hance its impact and value to stakeholders.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Global Land use and Land Cover monitoring: From Data to Impact - Li
 ndsey Sloat
URL:https://pretalx.earthmonitor.org/gw2023/talk/8M7MXV/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-LZRCGE@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T100000
DTEND;TZID=Europe/London:20231006T103000
DESCRIPTION:In response to the global climate and sustainability crisis\, m
 any countries have expressed ambitions goals in terms of carbon neutrality
  and a green economy. In this context\, the European Green Deal comprises 
 several policy elements aimed to achieve carbon neutrality by 2050. \n \nE
 SA is initiating various efforts to leverage on space technologies and dat
 a and support various Green Deal ambitions. The ESA Space for Green Future
  (S4GF) Accelerator will explore new mechanisms to promote the use of spac
 e technologies and advanced modelling approaches for scenario investigatio
 ns on the Green Transition of economy and society. \n \nA central element 
 of the S4GF accelerator are the Green Transition Information Factories (GT
 IF). GTIF takes advantage of Earth Observation (EO) capabilities\, geospat
 ial and digital platform technologies\, as well as cutting edge analytics 
 to generate actionable knowledge and decision support in the context of th
 e Green Transition. \n \nA first national scale GTIF demonstrator has now 
 been developed for Austria. \nIt addressed the information needs and natio
 nal priorities for the Green Deal in Austria. This is facilitated through 
 a bottom-up consultation and co-creation process with various national sta
 keholders and expert entities. These requirements are matched with various
  EO industry teams that\n \nThe current GTIF demonstrator for Austria (GTI
 F-AT) builds on top of federated European cloud services\, providing effic
 ient access to key EO data repositories and rich interdisciplinary dataset
 s. GTIF-AT initially addresses five Green Transition domains: (1) Energy T
 ransition\, (2) Mobility Transition\, (3) Sustainable Cities\, (4) Carbon 
 Accounting and (5) EO Adaptation Services. \n \nFor each of these domains\
 , scientific narratives are provided and elaborated using scrollytelling t
 echnologies. The GTIF interactive explore tools allow various users to exp
 lore the domains and subdomains in more detail to investigate better under
 stand the challenges\, complexities\, and underlying socio-economic and en
 vironmental conflicts. The GTIF interactive explore tools combine domain s
 pecific scientific results with intuitive Graphical User Interfaces and mo
 dern frontend technologies. In the GTIF Energy Transition domain\, users c
 an interactively investigate the suitability of locations at 10m resolutio
 n for the expansion of renewable (wind or solar) energy production. The to
 ols also allow investigating the underlying conflicts e.g.\, with existing
  land uses or biodiversity constraints. Satellite based altimetry is used 
 to dynamically monitor the water levels in hydro energy reservoirs to infe
 r the related energy storage potentials. In the sustainable cities’ doma
 in\, users can investigate the photovoltaic installments on rooftops and a
 ssess the suitability in terms of roof geometry and expected energy yields
 . \n \nGTIF enables users to inform themselves and interactively investiga
 te the challenges and opportunities related to the Green Transition ambiti
 ons. This enables e.g. citizens to engage in the discussion process for th
 e renewable energy expansion or support energy start-ups to develop new se
 rvices. The GTIF development follows an open science and open-source appro
 ach and several new GTIF instances are planned for the next years\, addres
 sing the Green Deal information needs and accelerating the Green Transitio
 n. This presentation will showcase some of the GTIF interactive explore to
 ols and provide an outlook on future efforts.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:The ESA Green Transition Information Factories – using Earth Obse
 rvation and cloud-based analytics to address the Green Transition informat
 ion needs. - Patrick Griffiths
URL:https://pretalx.earthmonitor.org/gw2023/talk/LZRCGE/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-NPSCYF@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T104500
DTEND;TZID=Europe/London:20231006T105000
DESCRIPTION:The monitoring of soil organic carbon (SOC) in cropland is cruc
 ial\, as soil health plays an important role in ensuring sustainable agric
 ultural productivity and in reducing carbon emissions. Remote sensing tech
 nologies offer a promising opportunity to monitor soil properties on a lar
 ge scale. Bare soil maps can be derived from satellite images and used to 
 estimate soil properties\, like the SOC content\, based on spectral proper
 ties. However\, analyzing such data requires processing large amounts of i
 nformation from different sensors and time frames\, which can be challengi
 ng. The Framework for Operational Radiometric Correction for Environmental
  Monitoring (FORCE) offers an interface to handle and analyze satellite da
 ta cubes and has been successfully used to create analysis-ready data in n
 umerous studies. This work focuses on the generation of soil reflectance c
 omposites based on the FORCE data cube and the construction of a bare soil
  data cube to estimate trends of dynamic soil properties with spatio-tempo
 ral machine learning.  \nUsing FORCE\, the generation of bare soil maps is
  optimized for large areas and different time intervals. This can be chall
 enging\, as the soil properties across large scales are diverse\, especial
 ly in regions with extreme conditions. New methods like spatial filters an
 d dynamic thresholds are implemented to improve the representation of diff
 erent soil regions and to reduce the noise of the soil reflectance composi
 tes. The results are stored in a bare soil data cube which is then used as
  a basis for soil monitoring. By combining this information with other env
 ironmental variables\, such as climate and topology\, the data cube approa
 ch can provide a basis to map soil dynamics over time. The bare soil cube 
 is tested by predicting the SOC trends of cropland soils in Germany. Sampl
 es from the LUCAS campaigns\, as well as from the Agricultural Soil Invent
 ory in Germany (BZE-LW)\, are used to train and validate the models. The p
 roposed approach has the potential to greatly improve the predictions of d
 ynamic soil properties and to inform better management practices for susta
 inable land use\, climate protection\, and policymaking. Furthermore\, the
  use of bare soil data cubes allows for efficient storage\, retrieval\, an
 d analysis of large amounts of analysis-ready data\, making it a practical
  and scalable approach for soil monitoring on a regional or global scale.
DTSTAMP:20260415T003046Z
LOCATION:Poster presentation
SUMMARY:Monitoring Cropland SOC with Bare Soil Data Cubes - Felix Lobert\, 
 Tom Broeg
URL:https://pretalx.earthmonitor.org/gw2023/talk/NPSCYF/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-K9GBRM@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T105000
DTEND;TZID=Europe/London:20231006T105500
DESCRIPTION:Accurate mapping and monitoring of land dynamics is critical fo
 r climate change mitigation\, biodiversity conservation\, and epidemic pre
 vention. With increasing data availability and processing capacities\, the
 re is a growing desire to move from periodic mapping of forest disturbance
 s to continuous monitoring systems capable of providing timely information
  on forest disturbances to a variety of stakeholders. Many algorithms and 
 approaches have been proposed in the research community to address this ne
 ar real-time monitoring challenge. Their performance is typically demonstr
 ated based on case studies over test areas or simulated datasets.  However
 \, when it is available\, the software provided with the research papers o
 nly offers limited operational capacity. Individual software is often prim
 arily developed to support the research experiment and consequently not op
 timized for speed or deployment at scale. In addition\, implementation in 
 different programming languages or the absence of a common interface to op
 erate the algorithm make interoperability and comparisons exercises challe
 nging. Inspired by the great success of scikit-learn that provides a stand
 ard interface to a large pool of optimized models and is widely acknowledg
 ed as the *de-facto* standard for machine learning in python\, we develope
 d the nrt python package. The package is designed for near real-time monit
 oring of disturbances in satellite image time-series. Five monitoring algo
 rithms from the scientific literature on change detection (EWMA\, CuSum\, 
 MoSum\, CCDC\, IQR) are implemented and exposed via a common API (Applicat
 ion Programming Interface). All the provided algorithms are optimized for 
 fast computation thanks to the use of vectorized expressions or numba's JI
 T (Just In Time) compiler. Additionally\, ongoing monitoring instances can
  be saved to disk and reloaded anytime\, allowing for effortless operation
 al deployment. The presentation will detail the functionalities of the pac
 kage\, the characteristics of the implemented algorithms\, and illustrate 
 its potential via a regional deployment covering several Sentinel2 tiles. 
 The supporting ecosystem of tools\, composed of dashboards for interactive
  use\, parameters selection\, and exploration of generated alert will also
  be presented.
DTSTAMP:20260415T003046Z
LOCATION:Poster presentation
SUMMARY:Streamlining forest disturbance monitoring over large areas with th
 e nrt python package - Loïc Dutrieux
URL:https://pretalx.earthmonitor.org/gw2023/talk/K9GBRM/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-UAZVRS@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T105500
DTEND;TZID=Europe/London:20231006T110000
DESCRIPTION:The United Nations (UN) 2030 Agenda aims at promoting sustainab
 le development at environmental\, social and economic level. The definitio
 n of the Sustainable Development Goals (SDGs) and of the associated Global
  Indicator Framework represent a data-driven effort\, helping countries in
  evidence-based decision-making and policies. SDG indicators’ monitoring
  and reporting across countries can benefit from substantial use of Earth 
 Observation (EO)\, including satellite and in-situ networks\, and of their
  processing through data analytics and numerical modeling approaches\, mak
 ing the 2030 Agenda implementation robust\, viable and faster\, both techn
 ically and financially.\n\nThis talk introduces SDGs-EYES\, a major new Eu
 ropean initiative aiming at boosting the European capacity for monitoring 
 the UN SDGs. SDGs-EYES addresses current gaps in the UN SDGs monitoring by
  exploiting data and information coming from the European Copernicus Progr
 amme and by providing a scientific and technological platform for building
  indicators through the integration of EO data\, advanced numerical modeli
 ng\, data analytics and Machine Learning approaches. Furthemore\, SDGs-EYE
 S aims to build a portfolio of decision-making products and services for t
 he assessment and monitoring of SDG indicators whose trends could impact t
 he environment and the society from an inter-sectoral perspective\, aligni
 ng with the EU Green Deal priorities and challenges.  \n\nThe SDGs-EYES sc
 ientific approach and framework are introduced and described with particul
 ar reference to three interconnected SDGs\, specifically on climate (SDG13
 )\, ocean (SDG14) and land (SDG15). These SDGs are mostly focused on the b
 iosphere as foundation of prosperity\, development and co-benefits in the 
 society and economy\, but also relevant due to their nexus with additional
  SDGs\, targets and indicators related to socio-economic and (geo)politica
 l factors (e.g.\, human health\, environmental crimes\, water and food ins
 ecurity\, poverty\, conflicts\, displacements\, migrations). \nFive Pilots
  (encompassing EU and extra-EU regions) will be used to demonstrate and va
 lidate the SDGs-EYES approach and results\, that is application-oriented s
 cientific products\, technological solutions and user-tailored services.
DTSTAMP:20260415T003046Z
LOCATION:Poster presentation
SUMMARY:SDGs-EYES: Strengthening the Monitoring of Sustainable Development 
 Goals through Copernicus Programme - Monia Santini\, Melissa	Latella
URL:https://pretalx.earthmonitor.org/gw2023/talk/UAZVRS/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-QSQKW7@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T111500
DTEND;TZID=Europe/London:20231006T113500
DESCRIPTION:Monitoring biodiversity in agricultural supply chains is a key 
 metric to assess progress on the EU's Biodiversity Strategy and the Farm t
 o Fork Policy. 10% of farms should be composed of 'high diversity landscap
 e features' - habitats such as treelines\, hedgerows\, semi-natural grassl
 and\, forests\, and wetlands. However\, most land cover mapping benchmark 
 datasets\, such as the recent OpenEarthMap\, fail to distinguish between a
 gricultural land (pasture\, arable\, forestry) and the semi-natural vegeta
 tion that counts towards the 10% target. Thus\, there is a lack of high-qu
 ality labelled data to develop models to measure progress towards importan
 t policy goals.\n\nI tested commercial high (SPOT 6/7) and very high resol
 ution (Pleiades) satellite images for their ability to pick up linear feat
 ures in Irish farmland\, and their ability to detect 10 landcover classes:
  Pasture\, Semi-Natural Woodland\, Conifer\, Scrub\, Hedgerow\, Semi-Natur
 al Grassland\, Artificial\, Bare Ground\, Shadow and Other. A minimum reso
 lution of 0.5m was required to accurately detect the linear features commo
 n to Irish Farmland. Due to a lack of high-quality masks that distinguish 
 farmed from semi-natural vegetation\, deep learning methods were unsuitabl
 e and so an object-based image analysis workflow was developed. The choice
  of segmentation parameters and number of segments were important to ensur
 e objects captured the shape of landscape features while minimising the sp
 eckle effect and loss of resolution due to segment size. Cloudless Pleiade
 s images for 40 farms distributed throughout the Republic of Ireland’s b
 iogeographic regions were obtained for the summer of 2022. Each image unde
 rwent segmentation and segments were labelled according to the 10 classes 
 above\, with ~1000 points per image to ensure data were obtained from each
  biogeographic regions in Ireland\, resulting in 80\,000 data points for m
 odel development.\n\nVarious indices (NDVI\, EVI 1-3\, NDWI\, GRVI\, CVI\,
  CCI\, CIGreen) and textures (Grey-level Co-Occurrence Matrices and Local 
 Binary Patterns) applied to indices and the pan chromatic band were added 
 as additional features. A model comparison procedure was carried out\, opt
 imising for balanced accuracy\, comparing random forests\, support vector 
 machines\, multi-layer perceptron\, kNN\, and multi-class logistic regress
 ion. A minimum balanced accuracy of 80% was considered acceptable for moni
 toring purposes. Random forests performed best\, with an out-of-sample bal
 anced accuracy of 82%. \nThe modelling framework and dataset can be used t
 o monitor progress towards the Green Deal targets\, and pilots monitoring 
 the biodiversity in large dairy-processors with significant supply chains 
 will be presented. Further improvements such as annual change detection an
 d extending to other European countries will be discussed.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Monitoring Biodiversity in Agricultural Supply Chains: an Irish Cas
 e Study - Cian White
URL:https://pretalx.earthmonitor.org/gw2023/talk/QSQKW7/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-LNKM3B@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T111500
DTEND;TZID=Europe/London:20231006T121500
DESCRIPTION:The Open-Earth-Monitor project aims to maximize the impact and 
 uptake of FAIR environmental data. In the framework of the stakeholder eng
 agement strategy\, an online survey on FAIR environmental data was impleme
 nted to get a comprehensive picture of whether the geospatial community is
  aware of FAIR data principles and what importance is attached to each pri
 nciple. During the workshop\, first results will be presented and discusse
 d with stakeholders. To collect their expectations and requirements for FA
 IR environmental data\, different perspectives from the geospatial communi
 ty should be represented in order to consider divergent opinions from data
  users and data providers.\nFurthermore\, the participants will get inform
 ed about the FAIR principles and further principles within the open data m
 ovement such as CARE (CARE Principles for Indigenous Data Governance) and 
 TRUST (TRUST Principles for digital repositories). The CARE Principles go 
 beyond FAIR to consider and protect the rights and interests of indigenous
  people and for this reason is also of great importance in the geospatial 
 data context. The TRUST principles introduce a framework to develop best p
 ractices for digital repositories to provide access to resources and enabl
 e users to rely and manage the respective data. Thus\, many of these princ
 iples are relevant for the ten GEO Data Management Principles (GEO DMP). T
 he GEO DMP were specifically designed for geospatial and environmental dat
 a. They define the data management requirements to facilitate and share Op
 en Data promptly and at minimum cost. Good data management implies a numbe
 r of activities to ensure that data are discoverable and accessible\, unde
 rstandable\, usable and maintained.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Conference Hall
SUMMARY:FAIR geospatial data: what stakeholders need and expect - Nora Meye
 r zu Erpen\, Nuno Queiroz Mesquita Caser de Sa
URL:https://pretalx.earthmonitor.org/gw2023/talk/LNKM3B/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-ZBUYJ9@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T111500
DTEND;TZID=Europe/London:20231006T120000
DESCRIPTION:This workshop will show the current tools we have available at 
 IIASA to generate reference data for training and validation to be used in
  ML models and to assess the accuracy and performance of the output produc
 ts the different OEMC monitors are going to generate. We show how\, on the
  one hand\, these tools can be used by experts\, as well as\, the crowd or
  citizens. The tools being presented will be geo-wiki\, picture pile as we
 ll as a newly developed google streetview in-situ tool. Examples of how th
 ese tools can be used for monitoring drivers of deforestation\, improving 
 forest management information as well as crop type mapping. Examples from 
 previous projects and applications will be shown. We furthermore demo two 
 deep dives on how citizen contributions in particular picture pile can hel
 p to collect reference data for the crop monitor. Some examples from an ex
 isting ESA project Crowd2Train will be featured. We furthermore show how p
 otentially the near real-time forest disturbance monitor (RADD Alerts) can
  be used in combination with picture pile to\, on the one hand\, increase 
 the confidence in those alerts and\, on the other hand\, how such approach
 es can potentially help to raise awareness about deforestation issues for 
 the wider public.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Seminar room 2 & 3
SUMMARY:In-situ Citizen Science Data for Training and Validation of the OEM
 C Monitors and other EO Mapping Models - Milutin Milenković\, Johannes Re
 iche
URL:https://pretalx.earthmonitor.org/gw2023/talk/ZBUYJ9/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-SBBZZL@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T114500
DTEND;TZID=Europe/London:20231006T120500
DESCRIPTION:Climate change\, environmental degradation and invasive species
  represent imminent threats to biodiversity. Decision makers urgently need
  accurate and reliable information about status\, trends and impacts. To d
 o so data needs to be presented in an actionable and understandable format
 \, including measures of uncertainty.\n\nThe emerging challenge is to prod
 uce synthesised data products that can be used further by ecologists for p
 urposes such as distribution modelling and risk mapping\, and that can be 
 combined with other environmental data\, such as climate and land use data
 . Within the Group on Earth Observations Biodiversity Observation Network 
 (GeoBON)\, it has been proposed to create aggregated biodiversity “data 
 cubes” with taxonomic (what)\, spatial (where) and temporal (when) dimen
 sions (Kissling et al. 2018). The Biodiversity Building Blocks for Policy 
 project (B-Cubed)  will generate biodiversity data cubes at the desired sc
 ale\, automatically\, as often as needed and with minimal manual intervent
 ion. These cubes will be made available and citable using the Global Biodi
 versity Information Facility (GBIF) infrastructure. Aside from the technol
 ogical challenges\, there is a conceptual challenge to solve: how to deal 
 with the taxonomic\, temporal and spatial uncertainty of biodiversity occu
 rrence data?\n\nTaxonomic uncertainty manifests itself in the form of syno
 nymy. By trusting a taxonomy backbone such as the GBIF Taxonomy Backbone\,
  this source of uncertainty is reduced. The temporal uncertainty is typica
 lly lower than the granularity used for aggregation (e.g. year) and can ty
 pically be neglected. On the contrary\, the spatial uncertainty cannot be 
 neglected.\n\nMost commonly\, occurrences are either collected in square g
 rids of various dimensions or as points with an uncertainty radius (Bloom 
 et al. 2018). Therefore\, occurrences are not defined as points\, but as t
 wo dimensional shapes\, typically squares or circles. These rarely fit to 
 the same geographic grid systems for which environmental and landscape dat
 a are available. A common solution is to upscale the data to a coarse grid
 . However\, this inevitably reduces the spatial resolution of the data\, w
 hich may result in a loss of accuracy when using data for building indicat
 ors and models.\n\nTo account for spatial uncertainty we developed within 
 the Tracking Invasive Alien Species project (TrIAS) an algorithm (Oldoni e
 t al. 2020) to randomly choose a point within the square or the circle and
  assign the occurrence to the spatial cell this point belongs to. This cou
 ld however produce slightly different results with every round of drafting
  occurrence cubes. By creating an ensemble of cubes\, we could correctly p
 ropagate the uncertainty from the raw occurrence data to the calculation o
 f summary statistics\, such as the number of occupied grid cells by a spec
 ies (observed occupancy). Using Monte Carlo simulations with synthetic dat
 a we aim to determine the ensemble size\, i.e. the minimum number of cubes
  needed to robustly infer the average observed occupancy and its uncertain
 ty.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Biodiversity data cubes: spatial aggregation and uncertainty - Dami
 ano Oldoni
URL:https://pretalx.earthmonitor.org/gw2023/talk/SBBZZL/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-EEXY7H@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T120000
DTEND;TZID=Europe/London:20231006T124500
DESCRIPTION:Strap yourself in and join Martijn and Luka on an Epic journey 
 through the EcoDataCube\, an analysis-ready\, totally open multidimensiona
 l spatiotemporal data cube covering most of Europe! After explaining how l
 arge amounts of 30m and 10m earth observation data was aggregated\, gap-fi
 lled\, and used to create 20 annual land cover maps with 43 classes at 30m
  resolution\, they will show you how to access the 200+ cloud-optimized da
 ta sets yourself with your browser\, GIS\, and Python code.\nAny leftover 
 time will be used to discuss why no dataset is truly analysis-ready\, how 
 no map is perfect\, and a collaborative attempt to create the ultimate lan
 d cover legend.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Seminar room 2 & 3
SUMMARY:EcoDataCube.eu: An open environmental data cube for Europe - Martij
 n Witjes
URL:https://pretalx.earthmonitor.org/gw2023/talk/EEXY7H/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-FUHYVQ@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T121500
DTEND;TZID=Europe/London:20231006T123500
DESCRIPTION:Climate change heavily impacts the management of natural parks 
 and land reserves: the increase in temperature\, the change in seasons’ 
 rhythm and other factors affect the faunistic and green population balance
  of the parks and the actions that park management entities must undertake
  to mitigate the negative effects. By monitoring the vegetation status ove
 r time it is possible to create a model of interaction between the changed
  landscape and its users and to craft tools to support their management.\n
 \nThe talk will present the tools for the environmental management of natu
 ral parks developed by Fondazione Edmund Mach and Deda Next in project Hig
 hlander (https://highlanderproject.eu/). The main focus of the talk will b
 e on the practical nature of the use cases covered by the tools and on the
  attention to usability that have been put in their design.\n\nThe front-e
 nd of the tools is a simple HTML interface\, spatially enabled with OpenLa
 yers. The back-end is more diverse\, depending on the use case\, but it in
 cludes several data elaboration scripts\, GeoServer as the map server (htt
 ps://geoserver.org/) and the FROST implementation (https://github.com/Frau
 nhoferIOSB/FROST-Server) of OGC’s SensorThings API standard on IoT time 
 series data (http://docs.opengeospatial.org/is/15-078r6/15-078r6.html). \n
 \nThe use cases covered by the tools are the following:\n\n1.	Mountain pas
 ture monitoring\nRemote sensing data is used to calculate Spectral Vegetat
 ion Indices changes across different     years or during the same mountain
  pasture season\, providing useful information for a more sustainable past
 ure management.\n\n2.	Tree species classification and above-ground biomass
  prediction\nAirborne remote sensing data and field data are combined in o
 rder to produce tree species and aboveground biomass maps\, estimated for 
 each individual tree crown.\n\n3.	Physiological monitoring of trees\nReal-
 time high-frequency measurements are provided at single-tree level by Tree
 Talker sensors. Data gathered (including leaf reflectance\, trunk growth\,
  water usage\, soil and stem humidity\, air temperature and plant stabilit
 y) can be used to understand the real-time response of trees to climate.\n
 \n4.	Forest windthrows detection and damages estimation \nWindthrows maps 
 are produced from high-resolution satellite images\, using as test event t
 he storm occurred in Vaia\, northeastern Italy\, at the end of October 201
 8 with wind gusts of 200 km/h. \n\n5.	Grassland mowing detection\nThe dete
 ction of mowing frequency is based on time series analysis of vegetation i
 ndexes derived from satellite imagery and provides an assessment at parcel
  level that can be compared with ground surveys.\n\n6.	Bark beetle detecti
 on and forest stress monitoring\nMany bark beetle species feed on weakened
 \, dying or dead spruce\, fir and hemlock. Thus the massive amount of fall
 en trees due to storm events represents an high risk condition for prolife
 ration. This tool estimates the locations most impacted by bark beetle pro
 liferation\, providing also a confidence level.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Novel practical tools for the environmental management of natural p
 arks - Luca Giovannini
URL:https://pretalx.earthmonitor.org/gw2023/talk/FUHYVQ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-LMD9DZ@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T134500
DTEND;TZID=Europe/London:20231006T140500
DESCRIPTION:African forest are increasingly in decline as a result of land-
 use conversion due to human activities. However\, a consistent and detaile
 d characterization and mapping of land-use change that results in forest l
 oss is not available at the spatial-temporal resolution and thematic level
 s suitable for decision-making at the local and regional scales\; so far t
 hey have only been provided on coarser scales and restricted to humid fore
 sts. Here we present the first high-resolution (5 m) and continental-scale
  mapping of land use following deforestation in Africa\, which covers an e
 stimated 13.85% of the global forest area\, including humid and dry forest
 s. We use reference data for 15 different land-use types from 30 countries
  and implement an active learning framework to train a deep learning model
  for predicting land-use following deforestation with an F1-score of 84%  
 for the whole of Africa. Our results show that the causes of forest loss v
 ary by region. In general\, small-scale crop-land is the dominant driver o
 f forest loss in Africa\, with hotspots in Madagascar and DRC. In addition
 \, commodity crops such as cacao\, oil palm\, and rubber are the dominant 
 drivers of forest loss in the humid forests of western and central Africa\
 , forming an "arc of commodity crops" in that region. At the same time\, t
 he hotspots for cashew are found to increasingly dominate in the dry fores
 ts of both western and south-eastern Africa\, while larger hotspots for la
 rge-scale croplands were found in Nigeria and Zambia. The increased expans
 ion of cacao\, cashew\, oil palm\, rubber\, and large-scale croplands obse
 rved in humid and dry forests of western and south-eastern Africa suggests
  they are vulnerable to future land-use changes by commodity crops\, thus 
 creating challenges for achieving the zero deforestation supply chains\, s
 upport REDD+ initiatives\, and towards sustainable development goals.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Mapping the diversity of land use following deforestation across Af
 rica with active learning - Robert Masolele
URL:https://pretalx.earthmonitor.org/gw2023/talk/LMD9DZ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-KHQVSS@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T134500
DTEND;TZID=Europe/London:20231006T143000
DESCRIPTION:The presenters will demonstrate how to use available future pro
 jections of climatic data across different climate change scenarios to for
 ecast how Earth's surface will look in the future. The presentation will b
 e equally balanced between theory and practice: the theoretical part will 
 provide an overview of the <u><a href="https://www.wcrp-climate.org/wgcm-c
 mip">Coupled Model Intercomparison Project</a></u>\, focusing on the model
 s produced for IPCC AR5\, and of spatiotemporal modeling of vegetation. Th
 e practical part will explain how to combine Earth Observation data and ma
 chine learning to produce maps of the <u><a href="https://global-ecosystem
 s.org/page/typology">major biomes</a></u> for the future and how their dis
 tribution is expected to change according to the different climate change 
 scenarios.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Seminar room 2 & 3
SUMMARY:Predicting the future Earth under climate scenarios: an R tutorial 
 - Carmelo Bonannella
URL:https://pretalx.earthmonitor.org/gw2023/talk/KHQVSS/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-FJDWMD@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T141500
DTEND;TZID=Europe/London:20231006T143500
DESCRIPTION:Central Europe experienced a series of droughts and heat waves 
 between 2018 and 2020 which severely effected the forest ecosystems.The ca
 nopy cover loss has been mapped for Germany by [1] via the use of high spa
 tial optical images from the Sentinel-2 and Landsat-8 satellites.In this c
 ontribution we want to present the results of assessing deforestation with
  a complementary approach using Sentinel-1 C-Band SAR data. We use the Rec
 urrence Quantification Analysis (RQA) to derive a change metric which take
 s the order of the time series into account [2]. This approach provides hi
 gh resolution yearly forest loss maps based on a continuous data stream.\n
 \nIn addition to the scientific results we showcase the processing pipelin
 e on the European Open Science Cloud. The amount of high resolution earth 
 observation data processed in this study was too large to do all analysis 
 on local computers or even local cluster systems. To achieve high performa
 nce computations for out-of-memory datasets we develop the YAXArrays.jl pa
 ckage in the Julia programming language. YAXArrays.jl provides both an abs
 traction over chunked n-dimensional arrays with labelled axes and efficien
 t multi-threaded and multi-process computation on these arrays. \n\nCitati
 on:\n\n[1]: Thonfeld\, F.\; Gessner\, U.\; Holzwarth\, S.\; Kriese\, J.\; 
 da Ponte\, E.\; Huth\, J.\; Kuenzer\, C.A First Assessment of Canopy Cover
  Loss in Germany’s Forests after the 2018–2020 Drought Years.\n\nRemot
 e Sens. 2022\, 14\, 562. https://doi.org/10.3390/rs14030562\n\n[2]:F. Crem
 er\, M. Urbazaev\, J. Cortés\, J. Truckenbrodt\, C. Schmullius and C. Thi
 el\, \n\n"Potential of Recurrence Metrics from Sentinel-1 Time Series for 
 Deforestation Mapping\," \n\nin IEEE Journal of Selected Topics in Applied
  Earth Observations and Remote Sensing\, vol. 13\, pp. 5233-5240\, 2020\, 
 https://doi.org/10.1109/JSTARS.2020.3019333
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Forest loss mapping based on Sentinel-1 time series - Felix Cremer\
 , Fabian Gans
URL:https://pretalx.earthmonitor.org/gw2023/talk/FJDWMD/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-KMZSRR@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T143000
DTEND;TZID=Europe/London:20231006T151500
DESCRIPTION:The OpenClimate Network is an open source nested accounting pla
 tform allows users to navigate emissions inventories and climate pledges o
 f different actors at every level\, aggregating data from various public s
 ources for countries\, regions\, cities and companies. Through this aggreg
 ation\, it enables the comparison of how different data sources report emi
 ssions of certain actors\, by harmonizing the way data is reported and ide
 ntifying the different methodologies used.\n\nAdditionally\, by nesting ac
 tors into their respective jurisdictions it facilitates the comparison bet
 ween the pledges these actors have committed to\, and to see if they are a
 ligned towards the same climate targets\, and how these compare to the goa
 ls of the Paris Agreement.\n\nBy aggregating data and exploring it in this
  nested manner\, it also allows for the effective identification of data g
 aps for these actors\, suggesting where efforts are needed to identify exi
 sting data sources or help produce new inventories. When data gaps are ide
 ntified\, the platform also prompts users to contribute data based on the 
 open and standardized data model used to aggregate emissions and pledges d
 ata.\n\nSpatial data can be a key component in tackling double counting\, 
 building subnational emissions inventories and accounting for corporate em
 issions.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Seminar room 2 & 3
SUMMARY:Using spatial data to build a nested climate accounting network - J
 oaquin van Peborgh
URL:https://pretalx.earthmonitor.org/gw2023/talk/KMZSRR/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-KBL7ML@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T144500
DTEND;TZID=Europe/London:20231006T150500
DESCRIPTION:Land use monitoring using machine learning and Earth observatio
 n data is usually challenging due to the lack of training samples\, especi
 ally for large areas and long periods where gathering in-situ information 
 is costly or sometimes impossible. This work proposes a machine learning a
 pproach called Time-Weighted Dynamic Time Warping (TWDTW) for data-scarce 
 applications. TWDTW is a satellite image time series classification algori
 thm that uses a Dynamic Time Warping (DTW) distance. DTW is a widely used 
 algorithm in various fields\, including speech recognition\, medicine\, in
 dustry\, and finance\, and has shown promising results in land use mapping
  due to its ability to deal with gaps in time series\, robustness to noise
 \, matching time series of different lengths and intervals\, and to keep i
 ts classification performance on small training sets.\n\nHowever\, DTW has
  limitations in matching events regardless of when they occur\, which can 
 result in out-of-season alignments and misclassifications—for example\, 
 aligning a summer crop to a winter one. TWDTW overcomes this limitation by
  introducing a time weight to matches deviating from an expected date in t
 he training set. This temporal constraint improves classification performa
 nce by controlling for out-of-season alignments while keeping DTW's flexib
 ility to smaller phenological fluctuations of vegetation.\n\nThis presenta
 tion will demonstrate the effectiveness of the TWDTW method for land use c
 lassification using the open-source R package dtwSat. Overall\, this machi
 ne learning method is suitable for data-scarce regions and can contribute 
 to land use monitoring\, supporting the environmental targets proposed by 
 the European Green Deal and the United Nations' Sustainable Development Go
 als.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Overcoming Data Scarcity in Land Use Monitoring with Time-Weighted 
 Dynamic Time Warping - Victor Maus
URL:https://pretalx.earthmonitor.org/gw2023/talk/KBL7ML/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-gw2023-UDY88D@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231006T163000
DTEND;TZID=Europe/London:20231006T170000
DESCRIPTION:As we enter a period of unprecedented global environmental cris
 es\, the importance of environmental research has never been more evident.
  The new keywords of our time are increasingly worrying: rapid change\, ad
 aptation\, resilience\, tipping points\, collapse.\n\nManaging the transit
 ion of our societies to new global climates is a major challenge for decis
 ion-makers at national and international levels. How to design\, implement
  and monitor effective policies that are economically and socially sustain
 able in the short term\, and environmentally effective in the long term? O
 ver the past centuries\, science has provided the knowledge that has led t
 o the current environmental crises (e.g. from the industrial revolution an
 d the use of fossil fuels to the widespread use of chemicals). Today\, sci
 ence is the only way to support a knowledge-based transformation of our en
 ergy systems\, economy and society in an environmentally and climate-susta
 inable way.\n\nPromoting knowledge-based transformations is a road full of
  obstacles that more open science\, FAIR data\, shared models and interdis
 ciplinary collaboration will help to overcome.\n\nTo name a few: the intri
 nsic complexity of the Earth system\, the uncertainty of predictions due t
 o the interconnectedness of the system and the non-linearity of its respon
 ses\, the mismatch between the time and time scales of actions and effects
 \, and\, on the social side\, the conflicts and polarisation fostered by t
 he media.\n\nFor each of these complexities that limit our ability to deal
  with environmental crises\, to mitigate them\, and to understand their im
 pacts\, ideas and examples will be presented on how Open Data and Open Sci
 ence are contributing\, and can contribute further.
DTSTAMP:20260415T003046Z
LOCATION:EURAC Auditorium
SUMMARY:Open Science in a Time of Environmental Crisis - Alessandro Cescatt
 i
URL:https://pretalx.earthmonitor.org/gw2023/talk/UDY88D/
END:VEVENT
END:VCALENDAR