0.9 opengeohub-summer-school-2025 2025-09-01 2025-09-06 6 00:05 https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/schedule/ Europe/Amsterdam 2025-09-01T09:00:00+02:00 09:00 01:30 HugoTECH opengeohub-summer-school-2025-372-plenary-introduction-including-introduction-to-the-hackathons https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/Y9SSP9/ false Tutorials en Plenary introduction Plenary introduction Plenary introduction Plenary introduction Plenary introduction Plenary introduction Plenary introduction Plenary introduction Plenary introduction Plenary introduction Plenary introduction Plenary introduction Plenary introduction 2025-09-01T11:00:00+02:00 11:00 00:30 HugoTECH opengeohub-summer-school-2025-382-spatiotemporal-machine-learning-15-practical-lessons-on-how-to-organize-monitoring-modeling-and-updating-of-predictions https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/THLTRJ/ false Theoretical sessions Keynote en Training points (ground observations and measurements of environmental variables) that are referenced in space and time, and are available for longer periods, can be used to build spatiotemporal Machine Learning models (stmlm). Such stmlm's can then be used to generate time-series of predictions, which can then be used to run time-series analysis. Spatiotemporal modeling is different from purely spatial mapping is in the following three aspects: (1) points and covariate layers are matched in spacetime (usually a day or month-year period of ground observations or at least the year of ground observations); (2) covariate layers are based on time-series of usually EO-based images (spatiotemporal data cubes) and include also accumulative indices (e.g. cumulative rainfall, cumulative snow cover, cumulative cropping fraction, and similar) and derivatives; (3) during model training and validation, points are subset in both spacetime to avoid overfitting and bias in predictions. This talk will address 15 practical lessons from running stml including how to organize monitoring networks, how to prevent overfitting, how to derive prediction errors in spacetime, how to use time-series of predictions to detect changes and similar. For more details see: https://www.nature.com/articles/s41467-022-32693-3 Tom Hengl (OpenGeoHub) 2025-09-01T11:30:00+02:00 11:30 00:30 HugoTECH opengeohub-summer-school-2025-364-monitoring-global-grassland-and-pasture-areas-an-integrated-approach-based-data-fusion-and-regional-calibration https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/PAUHGU/ false Theoretical sessions Keynote en Global Pasture Watch (GPW) initiative addresses the critical need for monitoring grasslands and pastures, which cover 40% of Earth's surface and are vital for carbon sequestration, food, biodiversity, and cultural heritage. GPW is developing four global, consistent, time-series datasets (2000 to 2024+): 30-m grassland class and extent, 30-m short vegetation height, 1-km livestock densities, and 30-m bi-monthly gross primary productivity. These flexible datasets serve as building blocks, enabling local calibration and fusion with other land cover products. For example, the GPW products can be combined into a harmonized map that identifies active grazing areas and pastures with varying management intensities, using global and continental thresholds for grassland classes, livestock densities, heights, and productivity trends, incorporating per-pixel uncertainty information. This approach relies on open data and open-source solutions, making it adaptable for various regional and local contexts. GPW also uses online tools like Geo-Wiki to collect and incorporate user feedback for future product enhancements. All data, including reference samples, are publicly available in cloud-native formats on platforms like Zenodo, STAC, and Google Earth Engine, with the source code accessible on GitHub https://github.com/wri/global-pasture-watch. Leandro Parente 2025-09-01T13:30:00+02:00 13:30 01:30 HugoTECH opengeohub-summer-school-2025-288-introduction-of-cloud-native-vector-format-hands-on-in-python-environment https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/C9G99Y/ false Python Tutorials en As vector data size increases, the demand of cloud computing is also getting more attention. This lecture aims to tackle the big vector data problem and how to tackle it in the cloud environment. The introduction of cloud-native vector formats such as Flatgeobuf, GeoParquet and PMtile. We will have the hand-on visualization and process the big vector dataset such as ICESat-2 and GEDI. The introduction and hand-on will combine with explanation of Vector Tile and Lazy Loading theories behind. The first half is the lecture about the theory and algorithm behind the cloud-native format and spatial indexing. The duration is about 30 minutes. The second half is hands-on using DuckDB, Polars, and other python packages to work with cloud-native format. In addition, we also will go through the parallel processing for big vector data, including parallelization using semaphore for writing in a single big file and partitioning big data into smaller chunks in parallel. Other links: https://docs.google.com/presentation/d/1B-Z7PPErQfGBqlhqdBn1tJ0CRov9e2XJWHi_Bio1QwA/edit?usp=drive_link The tutorial is co-hosted by Yu-Feng HO and Serkan Isik Yu-Feng Ho 2025-09-01T15:30:00+02:00 15:30 01:00 HugoTECH opengeohub-summer-school-2025-384-hackathon-workshops https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/DXWHKG/ false Hackathons Hackathon en Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops 2025-09-01T13:30:00+02:00 13:30 01:30 Expert Room 2 opengeohub-summer-school-2025-286-automatic-acquisition-and-processing-of-satellite-data-in-r https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/LCUE87/ false R training Tutorials en This practical workshop will introduce participants to the basics of acquiring and processing satellite data using the R programming language, with a focus on the popular `terra` and `rstac` packages. The workshop is designed for beginners with some very basic R knowledge who want to incorporate remote sensing data into their research. Participants will learn how to search, access, and download satellite imagery from various providers through the SpatioTemporal Asset Catalog (STAC) using the `rstac` package and how to handle spatial data using the `terra` package, including reading and writing data, raster processing and visualization techniques. Through hands-on exercises, participants will gain experience working with real satellite datasets and creating efficient and reproducible workflows. Ultimately, the knowledge and skills learned during the workshop will help use these data and processing methods for own research or projects. Automatic acquisition and processing of satellite data in R using `rstac` and `terra` Other links: https://drive.google.com/file/d/1wcn3c63moIFeaG0Tnr-Yo-JtoROWWJLD/view?usp=drive_link https://drive.google.com/file/d/1iBylfHF_o-fLXr6LwOautpu08DWJK1an/view?usp=drive_link Krzysztof Dyba 2025-09-01T13:30:00+02:00 13:30 01:30 Expert Room 3 opengeohub-summer-school-2025-304-introduction-to-grass-gis-as-a-spatial-analysis-engine https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/LBXSVE/ false Tutorials en Project, Mapset, Region. Types of modules and their documentation. Pipeline programming This session provides an overview of GRASS GIS as a spatial analysis engine. The main concepts of the software are introduced with then a more detailed outline of the modules focused on Earth Observation products. Contents: - Essential GRASS concepts: - Project: and Coordinate Reference Systems - Mapset: user acces and permissions - Zone: the compute area - GRASS internal data structures: - Raster - Vector - Imagery - Dataset impot/export, making use of GDAL/OGR - Processing module families: - dataset management - processing - database management - The Earth Observation (Imagery) modules Luís Moreira de Sousa 2025-09-01T16:30:00+02:00 16:30 02:00 W-Invite opengeohub-summer-school-2025-370-research-speed-dating https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/VAVRCX/ false Social Event en Social event Social event Social event Social event Social event Social event Social event Social event Social event Social event Social event Social event 2025-09-02T09:00:00+02:00 09:00 00:30 HugoTECH opengeohub-summer-school-2025-362-learning-from-global-earth-observation-data https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/JE8JLY/ false Theoretical sessions Theoretical lectures en The volume of unlabeled Earth observation (EO) data is huge. To interpret this vast amount of data, efficient modelling approaches are needed that can generalize to large geographic areas and are robust to inherent noise. Data-driven approaches promise great potential for interpreting and combining data from different space missions. In this talk, I will present our work on global canopy height mapping (https://langnico.github.io/globalcanopyheight/) with optical satellite images and sparse spaceborne lidar data and discuss a recent project called MMEarth (https://vishalned.github.io/mmearth/) that explored multi-modal pretext tasks for learning representations that are suitable for a range of downstream tasks with limited training data. The volume of unlabeled Earth observation (EO) data is huge. To interpret this vast amount of data, efficient modelling approaches are needed that can generalize to large geographic areas and are robust to inherent noise. Data-driven approaches promise great potential for interpreting and combining data from different space missions. In this talk, I will present our work on global canopy height mapping (https://langnico.github.io/globalcanopyheight/) with optical satellite images and sparse spaceborne lidar data and discuss a recent project called MMEarth (https://vishalned.github.io/mmearth/) that explored multi-modal pretext tasks for learning representations that are suitable for a range of downstream tasks with limited training data. Nico Lang 2025-09-02T09:30:00+02:00 09:30 01:00 HugoTECH opengeohub-summer-school-2025-379-cloud-based-analysis-of-earth-observation-data-using-open-source-software https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/BDVFTR/ false Theoretical sessions Theoretical lectures en The lecture will address how we analyse Earth Observation data Today, local or cloud-based, and how we ended up in the current situation. It will also address to which extent cloud platforms are built on open source software, and how the Earth Observation community can continue working towards a scientific practice that is sustainable, FAIR, and that complies to open science requirements. The lecture will address how we analyse Earth Observation data Today, local or cloud-based, and how we ended up in the current situation. It will also address to which extent cloud platforms are built on open source software, and how the Earth Observation community can continue working towards a scientific practice that is sustainable, FAIR, and that complies to open science requirements. Edzer Pebesma 2025-09-02T11:00:00+02:00 11:00 01:00 HugoTECH opengeohub-summer-school-2025-376-what-does-high-resolution-mean-space-based-spectrometry-for-air-quality-climate https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/8ESEJW/ false Theoretical sessions Theoretical lectures en Together we will investigate what “high resolution” means by working through the specifications of a “truly” hyperspectral satellite. The primary aim is explain the methodology starting at instrument level to illustrate the data chain of how absorption lines result in a better understanding of the distribution and variability of trace gas species relevant to air quality and climate change. A short data interactive module will be presented to tie together the lecture. Deborah C. Stein Zweers 2025-09-02T13:30:00+02:00 13:30 01:30 HugoTECH opengeohub-summer-school-2025-380-raster-and-vector-data-cubes-in-r-and-python https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/ZFRDCW/ false R training Tutorials en The workshop will repeat some theory about what multidimensional arrays are in computer science as well as in R and Python, and discuss how they can be used for solving Earth Observations problems. In particular it will discuss the use of arrays for time series data, for spatial data (raster maps, stacks of raster maps), for spatiotemporal data (time series of raster maps or stacks), and for time series of feature (point or polygon) data (vector data cubes). All will be illustrated by examples in R and Python, in parallel. Examples will be (mostly) reproducible by participants. The workshop will repeat some theory about what multidimensional arrays are in computer science as well as in R and Python, and discuss how they can be used for solving Earth Observations problems. In particular it will discuss the use of arrays for time series data, for spatial data (raster maps, stacks of raster maps), for spatiotemporal data (time series of raster maps or stacks), and for time series of feature (point or polygon) data (vector data cubes). All will be illustrated by examples in R and Python, in parallel. Examples will be (mostly) reproducible by participants. Edzer Pebesma 2025-09-02T15:30:00+02:00 15:30 01:30 HugoTECH opengeohub-summer-school-2025-305-grass-gis-automation-for-earth-observation-with-python-r-and-julia https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/8A3E7Z/ false Julia Tutorials en Scripting with GRASS: Bash, R, Python, Julia (?), matching students’ preferences and EO theme This session is mean for students with a basic understanding of GRASS, its underlying concepts and data strucutres. In a mostly hands-on exercise, students will be automating several Earth Observation taks from a programming environment of their choice: Bash, Python, R, Julia (?). Contents: - The GRASS environment - Internal and external datasets - Access to Imagery catalogues - Using GRASS GIS modules with: - Python - R - Bash - Julia (?) Other links: https://drive.google.com/file/d/1f43r-dC3Q3PYbQTyePhBUzMZWLu_cAJu/view?usp=drive_link Luís Moreira de Sousa 2025-09-02T17:00:00+02:00 17:00 01:00 HugoTECH opengeohub-summer-school-2025-371-hackathon-consultation https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/XQYRQW/ false Hackathon en Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation 2025-09-02T11:00:00+02:00 11:00 01:00 Expert Room 2 opengeohub-summer-school-2025-307-overview-of-passive-optical-instruments-for-earth-observation https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/RELHCA/ false Theoretical sessions Theoretical lectures en In this lecture, an overview of the different types of passive optical instruments for earth observation is given. Their limitations are outlined, and possible future development paths of such instruments are discussed. Passive optical instruments on satellites such as spectrometers and radiometers detect sunlight reflected from the earth. They allow us to measure land and atmospheric properties for applications such as agriculture and climate science. In this lecture, at first the most prevalent types of such instruments are introduced, such as for example spectral band-pass imagers and grating spectrometers. Some of the design trade-offs and performance limitations are discussed, and possible sources of errors in available earth observation data are outlined. After that, more innovative instrument designs from the recent time are introduced. The lecture will finish with a speculation on the future of passive optical instruments for earth observation, which will likely see a deeper integration of instrument design and data science in the future. Ralf Kohlhaas 2025-09-02T13:30:00+02:00 13:30 01:30 Expert Room 2 opengeohub-summer-school-2025-295-crash-course-on-google-earth-engine-api https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/EWFPYV/ false Javascript Tutorials en This tutorial introduces the Google Earth Engine (GEE) API. Participants will learn to filter, process, and export images, extract insights from temporal data, and export results using the API. GEE is a cloud-based platform for geospatial analysis. GEE provides an API (application programming interface) for image collection manipulation, time series processing, and spatial data transformations. JavaScript is the primary scripting language in the GEE Code Editor, allowing users to directly interact with the API for advanced workflows. This course covers coding techniques for transforming image collections and analyzing time series. It starts with API interactions, including loading and visualizing satellite imagery. Then, it moves to collection transformations, where participants filter datasets, compute statistical reductions, and generate composites. Next, it covers time series analysis, allowing participants to extract trends and plot variations. Finally, the session covers exporting results for further use. By the end, participants will develop a structured approach to writing JavaScript scripts in GEE. They will understand how the API enables geoprocessing workflows. This course is for researchers and GIS professionals looking to use cloud computing for geospatial analysis. It focuses on practical applications and offers hands-on experience with the entire process. Other links: https://docs.google.com/presentation/d/1zCIssZLp7ckRakl5HosKLYy5OGlk-FZHKtWptF3IrPg/edit?usp=sharing /media/opengeohub-summer-school-2025/submissions/EWFPYV/gee_sticker_150px_p6uvB5m.png Mustafa Serkan Isik 2025-09-02T15:30:00+02:00 15:30 01:30 Expert Room 2 opengeohub-summer-school-2025-313-monitoring-eo-workflows-with-precision-for-better-performance-and-resource-utilization https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/PXMTDT/ false Tutorials en The tutorial will guide participants on effectively monitoring and benchmarking their analysis workflows in a reliable and reproducible way, enabling them to identify performance bottlenecks and explore potential solutions. Other links: https://doi.org/10.5281/zenodo.17036542 https://data.crib.utwente.nl/geobench/demo.zip I will provide a tutorial. The tutorial title is: "Monitoring Earth Observation workflows with precision for better performance and resource utilization." The tutorial will guide participants on effectively monitoring and benchmarking their analysis workflows in a reliable and reproducible way, enabling them to identify performance bottlenecks and explore potential solutions. Other links: https://drive.google.com/file/d/1FeANLwwcY5ALd9Hi_9uwkcCYfPPUw6of/view?usp=drive_link Serkan Girgin 2025-09-02T13:30:00+02:00 13:30 01:30 Expert Room 3 opengeohub-summer-school-2025-287-land-use-and-land-cover-classification-with-satellite-image-time-series-in-r https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/JWWLJK/ false R training Tutorials en This tutorial introduces `sits`, an R package for Land use and land cover (LULC) classification using satellite image time series. LULC classification are essential for environmental monitoring, agriculture, and climate change analysis. LULC maps provide key data for decision-making. `sits` offers an integrated framework to generate data cubes and classify satellite image time series. It provides an end-to-end solution integrating data management, machine learning, and validation. The course covers the full LULC classification workflow. It starts with building data cubes and organizing satellite images in a structured way. Then, it moves to extracting and preparing time series samples. Next, participants train machine learning models to recognize LULC patterns. After that, they classify new satellite data using trained models. Finally, they validate results by assessing classification accuracy. This course is for remote sensing experts, GIS professionals, and students interested in using machine learning for LULC analysis. It focuses on practical applications and offers hands-on experience with the entire process. /media/opengeohub-summer-school-2025/submissions/JWWLJK/sits_sticker_150px_OBXNql8.png Krzysztof Dyba 2025-09-02T15:30:00+02:00 15:30 01:30 Expert Room 3 opengeohub-summer-school-2025-289-geomorphometry-high-performance-computing-using-grass-gis-and-whitboxtools-in-python https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/FGVXKV/ false Python Tutorials en Digital Elevation Model provides knowledge of topography, hydrology, geomorphology. Geomorphometry is a domain to extract the land surface parameters from DEMs. This tutorial introduces leveraging the state-of-art geo-processing softwares to create spatial information from DEMs. The tutorial starts with the introduction of DEMs and provides a first fully opened ~30m digital terrain model as the material. A single COG DTM would be provided and the code to crop the small area of interest. Aftermath, we will run the geo-processing from GRASS and WhiteboxTools. The first part is local land surface parameters such as slope, hillshade, and northerness, etc. Equi7 projection system is implemented to create accurate parameters. The second part is regional surface parameters. We focus on running topographic wetness index, which involves generating specific catchment area and slope. The border effect would be introduced and we introduce the overlapping technique. Last but not least, we would provide the 15 land surface parameters of the world in COG for comparison with the generated result. Other links: https://docs.google.com/presentation/d/1xNoDH3HRkFhm7laYPmtit6j3fuPrEuAJoUnLoczcGC8/edit?usp=drive_link Yu-Feng HoMustafa Serkan Isik 2025-09-03T09:00:00+02:00 09:00 00:30 HugoTECH opengeohub-summer-school-2025-366-introduction-to-the-eo-council https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/BUPMPE/ false Theoretical sessions Keynote en Overview of earth observation activities in the Netherlands, focusing on solid earth and deformation Netherlands hosts a range of earth observation activities, and it is difficult for industry, government, students to find their way. There was a need to cooperate more and the Earth Observation Council was formed with representatives from different disciplines and organizations in the Netherlands. We aim to contribute to education through a separate summer school in the future. This presentation will show some of the earth observation activities in the Netherlands. the focus will be on Solid Earth, Ocean and Land surface; Atmosphere and Cryosphere have a separate lecture later in the summer school. Wouter van der Wal 2025-09-03T09:30:00+02:00 09:30 00:30 HugoTECH opengeohub-summer-school-2025-367-introduction-to-surf https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/NCL9GY/ false Theoretical sessions Keynote en To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided Haili Hu 2025-09-03T10:00:00+02:00 10:00 00:30 HugoTECH opengeohub-summer-school-2025-368-introduction-to-nso https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/FF88PV/ false Theoretical sessions Keynote en To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided To be provided 2025-09-03T11:00:00+02:00 11:00 01:00 HugoTECH opengeohub-summer-school-2025-312-data-use-in-remote-sensing-of-the-atmosphere https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/RF8TPX/ false Theoretical sessions Theoretical lectures en This lecture discusses the use of models and data processing methods for efficient use of large volumes of satellite data, with application to atmospheric remote sensing. The lecture will come with exercises to gain hands-on experience using some of the techniques that are discussed. With advancing satellite technology enabling improvements in spatial, temporal, and spectral resolution of every new missions that is launched, the data rate is rapidly increasing also. This calls for computationally efficient methods for processing the data that come down, preferably in near real time. The focus of this lecture is on the final steps in the data processing chain, where large volumes of data our combined using models to extract and interpret the most useful information in the data. We'll discuss data assimilation and inverse modeling methods that are used, for example, in weather forecasting and the estimation of air pollutant and greenhouse gas emissions. Recently, the toolbox is being enriched with machine learning methods that are starting to outcompete some of the conventional methods, not only in processing speed but even in forecasting skill. The lecture will come with tutorials offering the opportunity to gain hands-on experience with some of these techniques. Other links: https://docs.google.com/presentation/d/1hah19DnS-ivigQhh1PpsVdYE2Rxwdw4F/edit?usp=drive_link&ouid=102861749140673074260&rtpof=true&sd=true Sander Houweling 2025-09-03T13:30:00+02:00 13:30 01:30 HugoTECH opengeohub-summer-school-2025-283-space-time-mapping-at-30-m-resolution-based-on-landsat-arco-data-hands-on-for-real-case-applications-python- https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/GDNV9D/ true Python Tutorials en This tutorial will offer a practical, step-by-step guide on accessing and using Landsat ARCO data for space-time mapping. Participants will work with real data to generate 30-meter resolution maps of the Netherlands from 2000 to 2024. The session will cover the entire workflow, including data acquisition, preprocessing, spatial and temporal analysis, and visualization. All code will be based on the Scikit-map library. The workshop is designed to be interactive, with participants using either Google Colab or Docker containers to follow along. Key topics will include retrieving Landsat ARCO data, handling large-scale remote sensing datasets, applying spatial and temporal filters, and analyzing changes in land cover over time. The focus will be on implementing a reproducible workflow that can be adapted for similar projects. This session is intended for participants with some experience in remote sensing or geospatial analysis. Prior knowledge of Python and GIS tools will be useful but not strictly required. Throughout the session, there will be opportunities for discussion and troubleshooting. By the end of the workshop, participants will have a working pipeline for generating and interpreting time-series maps from Landsat ARCO data. All provided input data will also be openly available at global scale for large scale applications. This tutorial will offer a practical, step-by-step guide on accessing and using Landsat ARCO data for space-time mapping. Participants will work with real data to generate 30-meter resolution maps of the Netherlands from 2000 to 2024. The session will cover the entire workflow, including data acquisition, preprocessing, spatial and temporal analysis, and visualization. All code will be based on the Scikit-map library. The workshop is designed to be interactive, with participants using either Google Colab or Docker containers to follow along. Key topics will include retrieving Landsat ARCO data, handling large-scale remote sensing datasets, applying spatial and temporal filters, and analyzing changes in land cover over time. The focus will be on implementing a reproducible workflow that can be adapted for similar projects. This session is intended for participants with some experience in remote sensing or geospatial analysis. Prior knowledge of Python and GIS tools will be useful but not strictly required. Throughout the session, there will be opportunities for discussion and troubleshooting. By the end of the workshop, participants will have a working pipeline for generating and interpreting time-series maps from Landsat ARCO data. All provided input data will also be openly available at global scale for large scale applications. Davide Consoli 2025-09-03T11:00:00+02:00 11:00 01:00 Expert Room 2 opengeohub-summer-school-2025-311-discrete-global-grid-systems-awareness-raising-about-the-future-of-gis https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/FD3Z9H/ false Theoretical sessions Theoretical lectures en This introduction talk will introduce advantages, technical challenges and currently available softwares. A Conventional Coordinate Reference System (CRS) represents the Earth's surface as a continuous field of points for navigation and geometry, but this approach is less suitable to relate point observations to larger areas. In contrast, a Discrete Global Grid System (DGGS) partitions the planet into a hierarchical grid of discrete cells, optimizing data integration, processing, and analysis by using fixed spatial units instead of continuous coordinates. While potentially more effective for GIS applications, DGGS are not yet commonly used. However, several initiatives are trying to create standards and softwares that will enable the transition. This introduction talk will introduce advantages, technical challenges and currently available softwares. Davide ConsoliLuís Moreira de Sousa 2025-09-03T13:30:00+02:00 13:30 01:30 Expert Room 2 opengeohub-summer-school-2025-310-high-performance-computing-with-pyhton-rs-dat https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/9TLLZZ/ false Python Tutorials en This summer school session will introduce participants to a Python/Dask-based ecosystem, familiarizing them with the use of the Remote Sensing Deployable Analysis environmenT (RS-DAT) framework to scale EO and RS data analysis using HTC/HPC systems and associated storage resources. The session will cover the tools for data access, retrieval and storage, and demonstrate the scaling up of processing and analysis workflows focused on EO data-sets. Data volumes are increasing across the natural and engineering sciences, with this being particularly relevant in the context of remote-sensing (RO) and Earth observation (EO) data, which have become a mainstay in fields ranging from the geosciences to ‘green’ life sciences, agriculture, and even social sciences, as well as an invaluable tool in defining policy. Although a community driven software ecosystem has evolved to support exploitation of these data, existing and future workflows often must be scaled up beyond the computational and storage resources available in workstations, taxing the tooling available and presenting a challenge to the use of these data. In this regard, solutions using high-throughput and high-performance computing (HTC/HPC) systems, as an additional alternative to cloud-based solutions, are of particular relevance for the academic community. Offering full control over available hardware, software, and data, these systems are excellently suited to highly-customized academic workflows and can readily support the migration of existing workflows. Furthermore, they are generally available through national infrastructure providers on a merit-driven no-cost basis. This summer school session will introduce participants to a Python/Dask-based ecosystem, familiarizing them with the use of the Remote Sensing Deployable Analysis environmenT (RS-DAT) framework to scale EO and RS data analysis using HTC/HPC systems and associated storage resources. The session will cover the tools for data access, retrieval and storage, and demonstrate the scaling up of processing and analysis workflows focused on EO data-sets. Meiert Willem Grootes 2025-09-03T13:30:00+02:00 13:30 01:30 Expert Room 3 opengeohub-summer-school-2025-297-processing-pace-data-with-python-get-a-polarized-hyperspectral-view-of-the-earth https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/9G333N/ false Python Tutorials en This tutorial lets you dive into the world of data from PACE, NASA's Plankton, Aerosol, Clouds and ocean Ecosystem mission using Python notebooks with a focus on SPEXone. PACE, the NASA Plankton, Aerosol, Clouds and ocean Ecosystem satellite was launched in February 2024 and carries three scientific instruments. The Ocean Color Instrument takes hyperspectral data at a 1 km x 1 km resolution and 2-day global coverage with very high radiometric accuracy. The two multiangle polarimeters, HARP2 and SPEXone, yield polarized data at a 5 km x 5 km resolution. HARP2 has 10-60 viewing angles at 4 spectral bands, while SPEXone (developed in the Netherlands) takes hyperspectral data at 5 viewing angles. Together, these instruments generate an unprecedented dataset that enables the characterization of ocean, land, clouds and the atmosphere. This tutorial aims to give the audience a jump-start into using data from the PACE mission using Python notebooks. We will explore Level-1 data (calibrated radiometry and multi-angle polarimetry) to create (hyperspectral) images of the Earth and look at Level-2 data and beyond in order to view higher level geophysical products, such as maps of atmospheric properties (e.g. aerosol and clouds), oceanic properties (e.g. phytoplankton), and land (e.g. vegetation). Required background knowledge: some proficiency in Python and experience running Jupyter notebooks. It is advised to setup your python environment prior to the workshop in orde to not loose valuable time. Tutorial link: https://drive.google.com/drive/folders/1lg__rXY6ukO7u0L4StkEX2gG53n4wPOX /media/opengeohub-summer-school-2025/submissions/9G333N/pace_rotator_emblem_800w_800h-3_7YAz1YH.jpg Jeroen RietjensLaura van der Schaaf 2025-09-03T15:30:00+02:00 15:30 02:00 W-Invite opengeohub-summer-school-2025-369-nso-companies-fair-networking-borrel https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/YM3HPE/ false Social Event en Social event Social event Social event Social event Social event Social event Social event Social event Social event Social event Social event Social event Companies presentations: https://drive.google.com/drive/folders/1eCPN0qmIyh-4NcKYTU6B4ECJ8hlel8KC?usp=drive_link 2025-09-04T09:00:00+02:00 09:00 00:30 HugoTECH opengeohub-summer-school-2025-386-introduction-to-openeo-within-the-copernicus-data-space-ecosystem https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/XKNKGR/ false Theoretical sessions Keynote en Traditionally way to process Earth Observation (EO) data could be complex and time-consuming, requiring users to discover, download, and pre-process large datasets locally. Consequently, cloud-based processing platforms have emerged as a popular and standard solution. The Copernicus Data Space Ecosystem offers the openEO API as one such solution for seamless access and efficient analysis of Copernicus EO data on a cloud infrastructure. OpenEO is a community-driven standard that simplifies geospatial data access, processing, and analysis by offering a unified platform. It allows developers, researchers, and data scientists to use cloud-based resources and distributed computing environments to tackle complex geospatial challenges. By following FAIR principles (Findable, Accessible, Interoperable, and Reusable), openEO supports the global sharing and reuse of algorithms, enhancing collaboration and scalability. Other links: https://drive.google.com/file/d/1dNQM4vx55UHKPVpi-ErB6cDCZQ_XjU2G/view?usp=drive_link https://docs.google.com/presentation/d/1DPDmfqkY6tSXJo5OWWPffGZaPkiD0mAB/edit?usp=drive_link&ouid=102861749140673074260&rtpof=true&sd=true Pratichhya Sharma 2025-09-04T09:30:00+02:00 09:30 01:00 HugoTECH opengeohub-summer-school-2025-282-observing-earth-system-dynamics-on-weather-and-climate-scales https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/DMJANT/ true Theoretical sessions Theoretical lectures en Clearly, climate change produces more extreme weather and associated ocean anomalies, hence calling for 1) improved weather advisories in the future for loss reduction and 2) for climate projections for adaptation. A better understanding of earth system dynamics may provide benefits in both areas. Earth-orbiting satellites have this century been proven indispensible for monitoring the earth system, both for tracking the weather and for understanding earth system processes. The value of satellite observations is being further exploited by novel future satellite missions. An overview of needs and EO systems will be provided. Winds determine weather, hurricanes, waves and surges, energy production, ocean forcing, heat and carbon budgets, sea ice decline, climate change, andsoforth. Hence, satellite winds are extensively used by marine forecasters, in NWP, by oceanographers, wind engineers, off-shore industry, safety authorities and climate scientists alike. More dynamical information is becoming available due to new and extending virtual constellations of satellite wind-sensing instruments. This new information is hence beneficial for above-mentioned applications. However, it is clear that a remaining gap in the understanding of earth system dynamics remains in the intricate coupling between the atmosphere and the ocean and in the presentation this aspect will also be further elaborated. As an example of the interplay between different professional disicplines, a furthermore interesting and related topic is how to measure and monitor winds in hurricanes? - Earth system modelling of dynamics; - The dynamics and the scales of the ocean and the atmosphere; - The interactions at different scales; - Different variables and processes; - How to effectively measure dynamics for model initialization and for parameterized processes; - Data assimilation; how to relate models and observations? - What to measure? - Data science Ad Stoffelen 2025-09-04T11:00:00+02:00 11:00 01:00 HugoTECH opengeohub-summer-school-2025-377-discussion-panel-how-to-build-an-open-decentralized-eo-fediverse- https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/DQANYK/ false Theoretical lectures en Discussion panel: How to build an open decentralized EO fediverse? Discussion panel: How to build an open decentralized EO fediverse? Discussion panel: How to build an open decentralized EO fediverse? Discussion panel: How to build an open decentralized EO fediverse? Discussion panel: How to build an open decentralized EO fediverse? Discussion panel: How to build an open decentralized EO fediverse? Discussion panel: How to build an open decentralized EO fediverse? https://docs.google.com/presentation/d/19UDgFDm-kxDA6hj6CS0r15RNgXV4QV67SJKgLOFGJ8M/edit?usp=sharing 2025-09-04T13:30:00+02:00 13:30 01:30 HugoTECH opengeohub-summer-school-2025-318-build-advanced-eo-workflows-with-custom-functions-in-openeo-within-the-copernicus-data-space-ecosystem https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/8YHQU9/ false Python Tutorials en In the Earth Observation (EO) domain, data analysis ranges from simple image processing tasks like dilation to complex workflows involving Machine Learning (ML) and Deep Learning (DL). While openEO provides numerous features and functions for data analysis, but given the broad scope of the field, not all potential needs are covered out of the box. To address this, openEO supports User-Defined Functions (UDFs). These UDFs are implemented as standard Python scripts using libraries such as Xarray or Numpy. They allow users to implement custom workflows tailored to specific research requirements. Thus, in this course, we offer a high-level introduction to openEO with a focus on UDFs and include an example of an advanced EO workflow that applies this concept in practice. The only requirement for the participants would be: - Basic understanding of Earth Observation data - Basic knowledge of Python - An account in the Copernicus Data Space Ecosystem We will use the Jupyter Environment offered by the ecosystem. For users already familiar with these, they can already explore examples available here: https://github.com/Open-EO/openeo-community-examples/tree/main/python Other links: CDSE: https://dataspace.copernicus.eu/ CDSE documentation: https://documentation.dataspace.copernicus.eu/#/ openEO community examples: https://github.com/Open-EO/openeo-community-examples/tree/main/python MOOC on cubes and cloud: https://eo-college.org/courses/cubes-and-clouds/ Example on openEO load_stac: https://github.com/Open-EO/openeo-community-examples/tree/main/python/LoadStac APEx algorithm catalogue: https://algorithm-catalogue.apex.esa.int/ openEO algorithm plaza: https://marketplace-portal.dataspace.copernicus.eu/ Pratichhya Sharma 2025-09-04T15:30:00+02:00 15:30 01:30 HugoTECH opengeohub-summer-school-2025-365-automl-automating-machine-learning-applied-to-areal-regression-modeling https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/MTHQXF/ false Theoretical sessions Tutorials en In this tutorial, an AutoML framework (EvalML) will be used for modeling livestock density at polygon level (areal regression) and produce spatial predictions at pixel level. A compilation of subnational livestock census data will be used to retrieve the headcount information, while the grazing areas will be estimated based on GPW grassland class and extent. You will learn how to run benchmark, feature selection and hyper-parameter tuning for several modeling pipelines, as well as evaluate the model performance using robust spatial cross-validation. The final modeling output will be spatial layer with livestock density and headcount at 1-km for pilot area. Other links: https://docs.google.com/presentation/d/1qePs0puoVaVaVysztSlqLadb4_YIxQNf2JeuaF5GWlI Leandro Parente 2025-09-04T17:00:00+02:00 17:00 01:00 HugoTECH opengeohub-summer-school-2025-383-hackathon-consultation https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/9TY7AD/ false Hackathons Hackathon en Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation 2025-09-04T13:30:00+02:00 13:30 01:30 Expert Room 2 opengeohub-summer-school-2025-291-application-packages-in-the-xcube-ecosystem https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/UHZMFT/ false Python Tutorials en Introduces the xcube core libraries and associated software components, and presents recent developments in integrating OGC Earth Observation Application Packages into the xcube ecosystem. xcube is a mature and steadily growing data cube framework based around Python and xarray; the core data processing functionality is complemented by a wide selection of additional tools and associated libraries, including an interactive viewer, a versatile API server, and a range of data access plug-ins. This talk introduces the xcube ecosystem and presents a recent addition, xcengine, developed as part of the Open-Earth-Monitor Cyberinfrastructure project. xcengine turns Python Jupyter notebooks into self-contained ‘compute engines’ based on Docker containers. Compute engines can be run as OGC Earth Observation Application Packages to integrate them into OGC workflows; they can also be run in an interactive mode with an integrated xcube server and viewer, providing easy integration of user code with xcube's extensive API and visualization support. Other links: https://github.com/xcube-dev/summerschool25/blob/main/slides.pdf https://github.com/xcube-dev/summerschool25 https://xcube-dev.github.io/summerschool25/ Pontus Lurcock 2025-09-04T15:30:00+02:00 15:30 01:30 Expert Room 2 opengeohub-summer-school-2025-284-less-distorted-analyses-of-satellite-imagery-using-discrete-global-grid-systems-in-julia https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/DAN9MN/ true Julia Tutorials en Discrete Global Grid Systems (DGGS) have emerged as a transformative approach to minimizing spatial distortions in geospatial data processing. Unlike traditional methods that merely name locations, DGGS offer a highly efficient data structure capable of reducing storage requirements by up to 33% compared to the current Sentinel-2 UTM tiling grid. The performance of operations on DGGS native data cubes is intrinsically linked to the cell index, which plays a crucial role in data management and retrieval. Most DGGS implementations utilize a hierarchical one-dimensional index to name and sort cells, optimizing them for parent-child queries. This structure is particularly beneficial for operations such as upscaling and downscaling, which are essential for integrating data with varying spatial resolutions. However, many real-world applications, such as visualization, fluid dynamics, and convolutions, require efficient handling of distant neighbor queries based on spatial distances. These applications often rely on bounding boxes or moving windows, which are not optimally supported by traditional DGGS implementations. In response to these challenges, we introduce DGGS.jl, a Julia package specifically developed to create and utilize DGGS native data cubes optimized for distant neighbor queries. Our package employs the DGGRID Q2DI index to store data on a hexagonal ISEA4H grid, enabling compact and efficient data cube arrays. We have implemented methods to seamlessly convert raster data between geographic and Q2DI coordinates, access neighbor disks around a given cell, and visualize these data on a global scale. To demonstrate the practical application of DGGS.jl, we present a hands-on workshop of our Julia package DGGS.jl to convert traditional geographical data cubes into DGGS native data cubes. Furthermore, we show how to process those data cubes and how to access neighbors and regions around given coordinates. Around 33% of the Sentinel-2 data is duplicated (Bauer-Marschallinger and Falkner, 2023)​ that needs to be downloaded, stored, and processed. This workshop introduces Discrete Global Grid Systems (DGGS) as a way to store satellite imagery in an efficient and distortion-less way. Then, we learn how to convert and process DGGS native data cubes in a hands-on session using the Julia package DGGS.jl. Other links: https://docs.google.com/presentation/d/1WIEBoGG6Basbu_TBdtZRoru3swD3yo93/edit?usp=drive_link&ouid=102861749140673074260&rtpof=true&sd=true Study materials are uploaded at: https://gist.github.com/danlooo/02dda9b15c001cb384181dd50421329f https://danlooo.github.io/DGGS.jl /media/opengeohub-summer-school-2025/submissions/DAN9MN/dest_4GN0H4J.png Daniel LoosLuís Moreira de Sousa 2025-09-04T13:30:00+02:00 13:30 01:30 Expert Room 3 opengeohub-summer-school-2025-300-an-introduction-to-julia https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/ZTXMAE/ false Julia Tutorials en An introduction lecture to Julia, focused on the installation, usage and other first steps. Useful if you plan on following subsequent lectures/tutorials on Julia and have no previous experience. This lecture will introduce Julia as a programming language, and will have hands-on exercises to prepare you for the subsequent lectures using Julia. It will cover the installation of Julia, the installation of packages and environments, and using the geospatial stack. In between we will discuss the concepts that make Julia fast, and give you tips and tricks to work with Julia beyond the summer school. Maarten Pronk 2025-09-04T15:30:00+02:00 15:30 01:30 Expert Room 3 opengeohub-summer-school-2025-299-geomorphometry-in-julia https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/PYWBXY/ false Julia Tutorials en Lecture on geomorphometry and elevation modelling, alternated by hands-on exercises in Julia on downloading, visualizing and analyzing elevation models. We discuss (global) elevation modelling, including recent advances, and follow up with a hands-on session in Julia, where we will use Rasters.jl and Geomorphometry.jl to download, analyze and visualize a subset of a global elevation model. These operations include hydrological functions such as flow accumulation methods, and the differences between several common algorithms. The importance of the correct coordinate reference systems and cellsize of a raster will be demonstrated. Key (geospatial) concepts in Julia will be introduced, including the performance gains made possible with Julia, although prior exposure (can be by watching previous lectures) is expected. An introduction lecture to Julia will be given beforehand. Maarten Pronk 2025-09-05T09:00:00+02:00 09:00 01:00 HugoTECH opengeohub-summer-school-2025-363-learning-representations-from-engineering-features-to-engineering-pretext-tasks https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/UNKX8N/ false Theoretical sessions Theoretical lectures en Machine learning has become an important toolbox for analyzing complex Earth observation data to derive information from the raw data. In particular, supervised deep learning has achieved great success in solving EO tasks where the relationship between input and output is not clearly understood. However, applications with limited reference data cannot directly benefit from advances in supervised deep learning. This lecture will first introduce the concepts of supervised deep learning and then provide an overview of research in the field of self-supervised learning (SSL), which aims to learn transferable representations (i.e., features) from unlabeled data. Machine learning has become an important toolbox for analyzing complex Earth observation data to derive information from the raw data. In particular, supervised deep learning has achieved great success in solving EO tasks where the relationship between input and output is not clearly understood. However, applications with limited reference data cannot directly benefit from advances in supervised deep learning. This lecture will first introduce the concepts of supervised deep learning and then provide an overview of research in the field of self-supervised learning (SSL), which aims to learn transferable representations (i.e., features) from unlabeled data. Nico Lang 2025-09-05T11:00:00+02:00 11:00 01:00 HugoTECH opengeohub-summer-school-2025-293-synthetic-aperture-radar-system-specificaitons-and-performance-trade-offs https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/U88UXR/ false Theoretical sessions Theoretical lectures en Spaceborne Synthetic Aperture Radar systems are an important component of our Earth Observation space infrastructure. This tutorial will guide the students through the main parameters that define a SAR system (antenna dimensions, available average power, etc.) and link them to the key performance indicators (radiometric sensitivity, spatial resolution, ambiguities, etc). While users of Synthetic Aperture Radar data can often get away with ignoring how the data is generated, there are many cases in which a fundamental understanding of the trade-offs involved in the design or operation of a SAR system are relevant. This tutorial will - Introduce the concept of a Synthetic Aperture Radar - Discuss the relevant performance indicators, some more general like spatial resolution or image size, and some more SAR specific, such as the noise equivalent sigma zero (NESZ) or how well ambiguities are suppressed. - Link these performance indicators to fundamental system parameters, such as antenna dimensions and available power. The students will work with a Python notebook to explore interactively how changing system design (or processing) parameters impacts the imaging performance. Paco Lopez Dekker 2025-09-05T13:30:00+02:00 13:30 01:30 HugoTECH opengeohub-summer-school-2025-317-atmospheric-greenhouse-gases-and-the-determination-of-their-concentrations-through-satellite-observations https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/SGUUY7/ false Tutorials en The absorption of telluric greenhouse gases in the Earth's atmosphere and the methodology for determining the concentrations of CO 2 and CH 4 through satellite observations are the focal points of this tutorial. For this tutorial, relevant aspects are discussed in the lecture on Radiative Transfer. The tutorial will guide participants in simulating the solar spectrum that is reflected by the Earth's atmosphere and subsequently measured by satellites. Emphasis will be placed on the 1.6 µm spectral range that encompasses the absorption bands of CO 2 , CH 4 , and water vapor. It is used by several satellite missions to infer greenhouse gas abundances from radiometric observations. The model that we will use will incorporate fundamental instrument characteristics such as spectral resolution and sampling, facilitating a comparison between our simulation and actual observations obtained from the GOSAT satellite. By adjusting the input parameters of the model, we strive to enhance the concordance between the model's predictions and observed data, thereby allowing us to ascertain the total quantities of CO 2 and CH 4 present in the monitored atmosphere. Requirements for this tutorial include an updated installation of Python and foundational skills in Python programming. It is essential to ensure that the following libraries are correctly installed: netCDF4 or xarray, numpy, and scipy. The use of Jupyter notebooks is anticipated, so familiarity with utilizing notebooks in a web browser or Visual Studio would be beneficial. Other links: https://owncloud2.sron.nl/index.php/s/rtiUpCL11nxcwXm pw: Tutorial Jochen Landgraf 2025-09-05T15:30:00+02:00 15:30 01:30 HugoTECH opengeohub-summer-school-2025-378-closing-and-announcement-of-hackathon-winners https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/DVDQRH/ false Tutorials en Closing and announcement of hackathon winners Closing and announcement of hackathon winners Closing and announcement of hackathon winners Closing and announcement of hackathon winners Closing and announcement of hackathon winners Closing and announcement of hackathon winners Closing and announcement of hackathon winners Tom Hengl (OpenGeoHub) 2025-09-05T11:00:00+02:00 11:00 01:00 Expert Room 2 opengeohub-summer-school-2025-315-the-radiative-transfer-theory https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/F7A3SC/ false Theoretical sessions Theoretical lectures en Radiative Transfer Theory provides a robust framework for understanding how matter interacts with light, enabling us to interpret radiometric observations of planetary atmospheres. This theory allows us to explore photochemical processes and calculate heating rates in atmospheres, both of which are crucial for studying atmospheric chemistry and modeling the climate. Solving the Maxwell equations of electrodynamics and the Schrödinger equation of quantum mechanics can be quite complex, especially when dealing with intricate objects like planets. Instead, we focus on the Radiative Transfer Theory, which allows us to study radiometric observations of stellar objects and the Earth's atmosphere effectively. This theory combines aspects of both fundamental physics theories. In this lecture, we will explore this powerful theory, starting with the foundational radiative transfer equation and its applications to remote sensing of celestial objects and the Earth's atmosphere. Other links: https://owncloud2.sron.nl/index.php/s/gCxJuHn3MqcwqMD pw: RRT25 /media/opengeohub-summer-school-2025/submissions/F7A3SC/RTT_VSthRoc.jpg Jochen Landgraf 2025-09-05T13:30:00+02:00 13:30 01:30 Expert Room 2 opengeohub-summer-school-2025-285-cryosphere-tutorial-in-python https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/HWFDTU/ false Python Tutorials en This tutorial session is designed for Cryosphere applications, focusing on the processing and analysis of satellite remote sensing data over ice sheets and sea ice regions. The session will introduce key satellite missions, including Sentinel-1 SAR, CryoSat-2 radar altimetry, and ICESat-2 laser altimetry, with hands-on exercises in Python. Participants will gain experience in: Synthetic Aperture Radar (SAR) processing using Sentinel-1 data for ice type classification. Radar altimetry (CryoSat-2) data analysis, including elevation and freeboard estimation. Laser altimetry (ICESat-2) data processing, focusing on surface elevation change detection, and sea ice thickness estimation. The tutorial will cover Python-based tools and libraries, including rasterio, xarray, h5py, and geopandas, to facilitate the analysis of large-scale remote sensing datasets. By the end of the session, participants will be able to apply these techniques to monitor ice sheet dynamics, track sea ice changes, and assess the impact of climate variability on the cryosphere. The cryosphere plays a crucial role in Earth's climate system and sea level changes. Satellite remote sensing provides essential observations for understanding ice sheet dynamics, sea ice variability, and snowpack conditions. This Python-based tutorial introduces practical techniques for analyzing Sentinel-1, CryoSat-2, and ICESat-2 data in cryospheric studies. Topics Covered Sentinel-1 SAR Processing Accessing and preprocessing Sentinel-1 GRD and SLC data. Generating backscatter composites for sea ice classification. CryoSat-2 Radar Altimetry Analysis Understanding radar waveforms over ice surfaces. Deriving sea ice freeboard and thickness from CryoSat-2 Level-2 products. Comparing altimetry measurements with in-situ validation data. ICESat-2 Photon Counting Altimetry Extracting surface elevation from ICESat-2 ATL07 and ATL10 datasets. Estimating ice sheet elevation change and sea ice thickness. Visualizing ICESat-2 track data over polar regions. Tools & Libraries Participants will work with: rasterio, geopandas, shapely – for geospatial data handling. h5py, xarray, numpy – for working with large satellite datasets. matplotlib, cartopy, seaborn – for visualization of polar data. Lu Zhou 2025-09-05T13:30:00+02:00 13:30 01:30 Expert Room 3 opengeohub-summer-school-2025-375-processing-large-grids-and-raster-algebra-using-terra-package-r- https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/7WDD97/ false R training Tutorials en Terra package in R (developed by Robert Hijmans; https://rspatial.github.io/terra/) is one of the finest libraries in R for processing gridded/raster data. It is an evolved version of the raster package and is fit for use with large rasters including time-series of raster images / EO images. The tutorial will demonstrate some processing steps used to aggregate time-series of images in parallel, run space-time overlay, combine interpolation functions on raster images and do more complex raster algebra. More examples are available also via: https://opengeohub.github.io/spatial-prediction-eml/ Example code and data is available: https://doi.org/10.5281/zenodo.14833052 https://differ.blog/p/processing-large-rasters-using-terra-package-for-r-combining-tiling-69d47a https://drive.google.com/file/d/1DsSV7ip7--BoWZ8J3R2On1D3H0oJ4fvP/view?usp=drive_link https://drive.google.com/file/d/1TLUTVs2-rGCZwCGybmNGxQHqBA6G4Kkv/view?usp=drive_link Tom Hengl (OpenGeoHub)Alexandre Wadoux 2025-09-06T08:30:00+02:00 08:30 08:00 HugoTECH opengeohub-summer-school-2025-385-field-excursion-doorwerth-castle https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/HMUU8R/ false Social Event en Field Excursion - Doorwerth castle Field Excursion - Doorwerth castle Field Excursion - Doorwerth castle Field Excursion - Doorwerth castle Field Excursion - Doorwerth castle Field Excursion - Doorwerth castle Field Excursion - Doorwerth castle Field Excursion - Doorwerth castle Field Excursion - Doorwerth castle Field Excursion - Doorwerth castle Field Excursion - Doorwerth castle