2.0 -//Pentabarf//Schedule//EN PUBLISH Y9SSP9@@pretalx.earthmonitor.org -Y9SSP9 Plenary introduction, including introduction to the hackathons en en 20250901T090000 20250901T103000 1.03000

Plenary introduction, including introduction to the hackathons

Plenary introduction Plenary introduction Plenary introduction Plenary introduction Plenary introduction Plenary introduction Plenary introduction PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/Y9SSP9/ HugoTECH PUBLISH THLTRJ@@pretalx.earthmonitor.org -THLTRJ Spatiotemporal Machine Learning: 15 practical lessons on how to organize monitoring, modeling and updating of predictions en en 20250901T110000 20250901T113000 0.03000

Spatiotemporal Machine Learning: 15 practical lessons on how to organize monitoring, modeling and updating of predictions

For more details see: https://www.nature.com/articles/s41467-022-32693-3 PUBLIC CONFIRMED Keynote https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/THLTRJ/ HugoTECH Tom Hengl (OpenGeoHub) PUBLISH PAUHGU@@pretalx.earthmonitor.org -PAUHGU Monitoring global grassland and pasture areas: An integrated approach based data-fusion and regional calibration en en 20250901T113000 20250901T120000 0.03000

Monitoring global grassland and pasture areas: An integrated approach based data-fusion and regional calibration

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. PUBLIC CONFIRMED Keynote https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/PAUHGU/ HugoTECH Leandro Parente PUBLISH C9G99Y@@pretalx.earthmonitor.org -C9G99Y Introduction of cloud-native vector format: hands-on in Python environment en en 20250901T133000 20250901T150000 1.03000

Introduction of cloud-native vector format: hands-on in Python environment

The tutorial is co-hosted by Yu-Feng HO and Serkan Isik PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/C9G99Y/ HugoTECH Yu-Feng Ho PUBLISH DXWHKG@@pretalx.earthmonitor.org -DXWHKG Hackathon workshops en en 20250901T153000 20250901T163000 1.00000

Hackathon workshops

Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops Hackathon workshops PUBLIC CONFIRMED Hackathon https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/DXWHKG/ HugoTECH PUBLISH LCUE87@@pretalx.earthmonitor.org -LCUE87 Automatic acquisition and processing of satellite data in R en en 20250901T133000 20250901T150000 1.03000

Automatic acquisition and processing of satellite data in R

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 PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/LCUE87/ Expert Room 2 Krzysztof Dyba PUBLISH LBXSVE@@pretalx.earthmonitor.org -LBXSVE Introduction to GRASS GIS as a spatial analysis engine en en 20250901T133000 20250901T150000 1.03000

Introduction to GRASS GIS as a spatial analysis engine

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 PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/LBXSVE/ Expert Room 3 Luís Moreira de Sousa PUBLISH VAVRCX@@pretalx.earthmonitor.org -VAVRCX Research speed dating en en 20250901T163000 20250901T183000 2.00000

Research speed dating

Social event Social event Social event Social event Social event Social event PUBLIC CONFIRMED Social Event https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/VAVRCX/ W-Invite PUBLISH JE8JLY@@pretalx.earthmonitor.org -JE8JLY Learning From Global Earth Observation Data en en 20250902T090000 20250902T093000 0.03000

Learning From Global Earth Observation 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. PUBLIC CONFIRMED Theoretical lectures https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/JE8JLY/ HugoTECH Nico Lang PUBLISH BDVFTR@@pretalx.earthmonitor.org -BDVFTR Cloud-based analysis of Earth Observation data using open-source software en en 20250902T093000 20250902T103000 1.00000

Cloud-based analysis of Earth Observation data using open-source software

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. PUBLIC CONFIRMED Theoretical lectures https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/BDVFTR/ HugoTECH Edzer Pebesma PUBLISH 8ESEJW@@pretalx.earthmonitor.org -8ESEJW What does high resolution mean? Space-based spectrometry for air quality & climate en en 20250902T110000 20250902T120000 1.00000

What does high resolution mean? Space-based spectrometry for air quality & climate

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. PUBLIC CONFIRMED Theoretical lectures https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/8ESEJW/ HugoTECH Deborah C. Stein Zweers PUBLISH ZFRDCW@@pretalx.earthmonitor.org -ZFRDCW Raster and vector data cubes in R and Python en en 20250902T133000 20250902T150000 1.03000

Raster and vector data cubes in R and Python

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. PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/ZFRDCW/ HugoTECH Edzer Pebesma PUBLISH 8A3E7Z@@pretalx.earthmonitor.org -8A3E7Z GRASS GIS automation for Earth Observation with Python, R and Julia en en 20250902T153000 20250902T170000 1.03000

GRASS GIS automation for Earth Observation with Python, R and Julia

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 PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/8A3E7Z/ HugoTECH Luís Moreira de Sousa PUBLISH XQYRQW@@pretalx.earthmonitor.org -XQYRQW Hackathon Consultation en en 20250902T170000 20250902T180000 1.00000

Hackathon Consultation

Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation PUBLIC CONFIRMED Hackathon https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/XQYRQW/ HugoTECH PUBLISH RELHCA@@pretalx.earthmonitor.org -RELHCA Overview of passive optical instruments for earth observation en en 20250902T110000 20250902T120000 1.00000

Overview of passive optical instruments for earth observation

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. PUBLIC CONFIRMED Theoretical lectures https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/RELHCA/ Expert Room 2 Ralf Kohlhaas PUBLISH EWFPYV@@pretalx.earthmonitor.org -EWFPYV Crash Course on Google Earth Engine API en en 20250902T133000 20250902T150000 1.03000

Crash Course on Google Earth Engine 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 PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/EWFPYV/ Expert Room 2 Mustafa Serkan Isik PUBLISH PXMTDT@@pretalx.earthmonitor.org -PXMTDT Monitoring EO workflows with precision for better performance and resource utilization en en 20250902T153000 20250902T170000 1.03000

Monitoring EO workflows with precision for better performance and resource utilization

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 PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/PXMTDT/ Expert Room 2 Serkan Girgin PUBLISH JWWLJK@@pretalx.earthmonitor.org -JWWLJK Land Use and Land Cover Classification with Satellite Image Time Series in R en en 20250902T133000 20250902T150000 1.03000

Land Use and Land Cover Classification with Satellite Image Time Series in R

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. PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/JWWLJK/ Expert Room 3 Krzysztof Dyba PUBLISH FGVXKV@@pretalx.earthmonitor.org -FGVXKV Geomorphometry: high-performance computing using GRASS GIS, and WhitboxTools in Python en en 20250902T153000 20250902T170000 1.03000

Geomorphometry: high-performance computing using GRASS GIS, and WhitboxTools in Python

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 PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/FGVXKV/ Expert Room 3 Yu-Feng Ho Mustafa Serkan Isik PUBLISH BUPMPE@@pretalx.earthmonitor.org -BUPMPE Introduction to the EO Council en en 20250903T090000 20250903T093000 0.03000

Introduction to the EO Council

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. PUBLIC CONFIRMED Keynote https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/BUPMPE/ HugoTECH Wouter van der Wal PUBLISH NCL9GY@@pretalx.earthmonitor.org -NCL9GY Introduction to SURF en en 20250903T093000 20250903T100000 0.03000

Introduction to SURF

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Introduction to NSO

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Data use in remote sensing of the atmosphere

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 PUBLIC CONFIRMED Theoretical lectures https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/RF8TPX/ HugoTECH Sander Houweling PUBLISH GDNV9D@@pretalx.earthmonitor.org -GDNV9D Space-time mapping at 30 m resolution based on Landsat ARCO data: hands-on for real case applications (python) en en 20250903T133000 20250903T150000 1.03000

Space-time mapping at 30 m resolution based on Landsat ARCO data: hands-on for real case applications (python)

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. PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/GDNV9D/ HugoTECH Davide Consoli PUBLISH FD3Z9H@@pretalx.earthmonitor.org -FD3Z9H Discrete Global Grid Systems - Awareness raising about the future of GIS en en 20250903T110000 20250903T120000 1.00000

Discrete Global Grid Systems - Awareness raising about the future of GIS

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. PUBLIC CONFIRMED Theoretical lectures https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/FD3Z9H/ Expert Room 2 Davide Consoli Luís Moreira de Sousa PUBLISH 9TLLZZ@@pretalx.earthmonitor.org -9TLLZZ High performance computing with Pyhton/RS-DAT en en 20250903T133000 20250903T150000 1.03000

High performance computing with Pyhton/RS-DAT

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. PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/9TLLZZ/ Expert Room 2 Meiert Willem Grootes PUBLISH 9G333N@@pretalx.earthmonitor.org -9G333N Processing PACE data with Python: get a polarized hyperspectral view of the Earth en en 20250903T133000 20250903T150000 1.03000

Processing PACE data with Python: get a polarized hyperspectral view of the Earth

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 PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/9G333N/ Expert Room 3 Jeroen Rietjens Laura van der Schaaf PUBLISH YM3HPE@@pretalx.earthmonitor.org -YM3HPE NSO Companies Fair + Networking borrel en en 20250903T153000 20250903T173000 2.00000

NSO Companies Fair + Networking borrel

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 PUBLIC CONFIRMED Social Event https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/YM3HPE/ W-Invite PUBLISH XKNKGR@@pretalx.earthmonitor.org -XKNKGR Introduction to openEO within the Copernicus Data Space Ecosystem en en 20250904T090000 20250904T093000 0.03000

Introduction to openEO within the Copernicus Data Space Ecosystem

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 PUBLIC CONFIRMED Keynote https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/XKNKGR/ HugoTECH Pratichhya Sharma PUBLISH DMJANT@@pretalx.earthmonitor.org -DMJANT Observing earth system dynamics on weather and climate scales en en 20250904T093000 20250904T103000 1.00000

Observing earth system dynamics on weather and climate scales

- 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 PUBLIC CONFIRMED Theoretical lectures https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/DMJANT/ HugoTECH Ad Stoffelen PUBLISH DQANYK@@pretalx.earthmonitor.org -DQANYK Discussion panel: How to build an open decentralized EO fediverse? en en 20250904T110000 20250904T120000 1.00000

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 PUBLIC CONFIRMED Theoretical lectures https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/DQANYK/ HugoTECH PUBLISH 8YHQU9@@pretalx.earthmonitor.org -8YHQU9 Build Advanced EO Workflows with Custom Functions in openEO within the Copernicus Data Space Ecosystem en en 20250904T133000 20250904T150000 1.03000

Build Advanced EO Workflows with Custom Functions in openEO within the Copernicus Data Space Ecosystem

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/ PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/8YHQU9/ HugoTECH Pratichhya Sharma PUBLISH MTHQXF@@pretalx.earthmonitor.org -MTHQXF AutoML: Automating machine learning applied to areal regression modeling en en 20250904T153000 20250904T170000 1.03000

AutoML: Automating machine learning applied to areal regression modeling

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 PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/MTHQXF/ HugoTECH Leandro Parente PUBLISH 9TY7AD@@pretalx.earthmonitor.org -9TY7AD Hackathon Consultation en en 20250904T170000 20250904T180000 1.00000

Hackathon Consultation

Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation Hackathon Consultation PUBLIC CONFIRMED Hackathon https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/9TY7AD/ HugoTECH PUBLISH UHZMFT@@pretalx.earthmonitor.org -UHZMFT Application Packages in the xcube ecosystem en en 20250904T133000 20250904T150000 1.03000

Application Packages in 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/ PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/UHZMFT/ Expert Room 2 Pontus Lurcock PUBLISH DAN9MN@@pretalx.earthmonitor.org -DAN9MN Less distorted analyses of satellite imagery using Discrete Global Grid Systems in Julia en en 20250904T153000 20250904T170000 1.03000

Less distorted analyses of satellite imagery using Discrete Global Grid Systems in Julia

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 PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/DAN9MN/ Expert Room 2 Daniel Loos Luís Moreira de Sousa PUBLISH ZTXMAE@@pretalx.earthmonitor.org -ZTXMAE An introduction to Julia en en 20250904T133000 20250904T150000 1.03000

An introduction to Julia

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. PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/ZTXMAE/ Expert Room 3 Maarten Pronk PUBLISH PYWBXY@@pretalx.earthmonitor.org -PYWBXY Geomorphometry in Julia en en 20250904T153000 20250904T170000 1.03000

Geomorphometry in Julia

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. PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/PYWBXY/ Expert Room 3 Maarten Pronk PUBLISH UNKX8N@@pretalx.earthmonitor.org -UNKX8N Learning Representations: From Engineering Features to Engineering Pretext Tasks en en 20250905T090000 20250905T100000 1.00000

Learning Representations: From Engineering Features to Engineering Pretext Tasks

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. PUBLIC CONFIRMED Theoretical lectures https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/UNKX8N/ HugoTECH Nico Lang PUBLISH U88UXR@@pretalx.earthmonitor.org -U88UXR Synthetic Aperture Radar: system specificaitons and performance trade offs en en 20250905T110000 20250905T120000 1.00000

Synthetic Aperture Radar: system specificaitons and performance trade offs

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. PUBLIC CONFIRMED Theoretical lectures https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/U88UXR/ HugoTECH Paco Lopez Dekker PUBLISH SGUUY7@@pretalx.earthmonitor.org -SGUUY7 Atmospheric Greenhouse Gases and the Determination of Their Concentrations Through Satellite Observations en en 20250905T133000 20250905T150000 1.03000

Atmospheric Greenhouse Gases and the Determination of Their Concentrations Through Satellite Observations

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 PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/SGUUY7/ HugoTECH Jochen Landgraf PUBLISH DVDQRH@@pretalx.earthmonitor.org -DVDQRH Closing and announcement of hackathon winners en en 20250905T153000 20250905T170000 1.03000

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 PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/DVDQRH/ HugoTECH Tom Hengl (OpenGeoHub) PUBLISH F7A3SC@@pretalx.earthmonitor.org -F7A3SC The Radiative Transfer Theory en en 20250905T110000 20250905T120000 1.00000

The Radiative Transfer Theory

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 PUBLIC CONFIRMED Theoretical lectures https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/F7A3SC/ Expert Room 2 Jochen Landgraf PUBLISH HWFDTU@@pretalx.earthmonitor.org -HWFDTU Cryosphere Tutorial in Python en en 20250905T133000 20250905T150000 1.03000

Cryosphere Tutorial in Python

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. PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/HWFDTU/ Expert Room 2 Lu Zhou PUBLISH 7WDD97@@pretalx.earthmonitor.org -7WDD97 Processing large grids and raster algebra using terra package (R) en en 20250905T133000 20250905T150000 1.03000

Processing large grids and raster algebra using terra package (R)

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 PUBLIC CONFIRMED Tutorials https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/7WDD97/ Expert Room 3 Tom Hengl (OpenGeoHub) Alexandre Wadoux PUBLISH HMUU8R@@pretalx.earthmonitor.org -HMUU8R Field Excursion - Doorwerth castle en en 20250906T083000 20250906T163000 8.00000

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 PUBLIC CONFIRMED Social Event https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/HMUU8R/ HugoTECH