Sprint bdfs25
TorchGeo an OSGeo Project Adam Stewart lead POSTDOC
Additional Topics:
1. Workshops
Berkeley Climate AI Day LINK
ml4earth.de workshop featured a keynote by Xiaoxiang Zhu from TUM Germany, also home to the creator of TorchGeo. The discussion likely entered around the applications of artificial intelligence (AI) in Earth observation, with a focus on machine learning (ML) approaches for remote sensing.
https://ml4earth.de/workshop_2025/
http://dataspace.copernicus.eu/ https://eotdl.com
2. PANGAEA project
This large research initiative aims to provide public data for land cover detection across remote areas such as Africa, Amazonia, and Siberia. The conversation might have touched upon the importance of publicly available datasets for Earth observation research and the potential applications of this data.
https://bpa.st/BTQAE ## code updates
https://eotdl.com/blog/pangaea
[PANGAEA benchmark](https://github.com/VMarsocci/pangaea-bench) shows that specialized, not-CNN and not-ViT , machine learning models can perform better than current (trendy) "Foundation Models" for remote sensing data
3. Machine learning approaches
A key takeaway from this discussion is that traditional ML methods (e.g., XGBoost, Random Forest) often outperform trendy CNN/ViT models for remote sensing tasks. This highlights the value of specialized ML models over foundation models in this domain.
these (https://huggingface.co/ibm-esa-geospatial/TerraMind-1.0-base) mixed models are wild, though
like this one https://github.com/swiss-territorial-data-lab/proj-vit [DATA\_LINK](https://huggingface.co/datasets/heig-vd-geo/M3DRS)
https://iclr-blogposts.github.io/2025/blog/sparse-autodiff/
4. Data availability
The conversation likely emphasized the importance of public datasets for Earth observation research. For instance, projects like TerraMesh, funded by ESA (European Space Agency), provide valuable data sources for researchers and developers.
hm - on the US West Coast side - team MSFT, Planet Labs and The Nature Conservancy .. [D2](https://reatlas42216storage.blob.core.windows.net/public/wind_all_2024q2_3_11_2025.gpkg) [LINK](https://www.microsoft.com/en-us/research/wp-content/uploads/2025/03/Global-Renewables-Watch_Caleb-Robinson_2025.pdf) [DATA0]
project 2020 "[TerraMesh](https://openaccess.thecvf.com/content/CVPR2025W/EarthVision/html/Blumenstiel_TerraMesh_A_Planetary_Mosaic_of_Multimodal_Earth_Observation_Data_CVPRW_2025_paper.html) is part of the FAST‑EO project funded by the European Space Agency Φ‑Lab (contract #4000143501/23/I‑DT)."
hey - public training [data](https://x-ytong.github.io/project/Five-Billion-Pixels.html) for China interior, by Xiou xiang Zhu .. who is very much in Germany and is the boss of the department with the `torchGeo` guy
gdal containers LINK
opendatacube containers LINK
5. Geospatial data infrastructure
References to geospatial data infrastructure initiatives such as STAC (SpatioTemporal Asset Catalog) and OSGeoLive were likely discussed, highlighting the need for standardized data formats and efficient data access mechanisms.
Key points and takeaways:
Specialized ML models: Traditional ML methods can outperform trendy AI approaches in remote sensing tasks.Public data availability: Public datasets are essential for Earth observation research, enabling collaboration and innovation. Collaboration opportunities: The conversation likely touched upon the potential for international collaborations across different countries and regions. Geospatial data infrastructure: Standardized data formats and efficient data access mechanisms are crucial for geospatial research.
Preparation for Big Data from Space #osgeo code sprint
The participants' discussion is a preparation activity for an upcoming code sprint, where they will work together to develop innovative solutions using remote sensing and Earth observation datasets. A wiki page summarizing these ideas could be a valuable output from this collaboration.
Overall, the conversation highlights the intersection of geospatial technology, machine learning, and open science initiatives in Earth observation, emphasizing the importance of data availability, specialized ML models, and geospatial data infrastructure for advancing research and innovation in this field.