[[File:yaosgeologo.jpg|thumb]]

'''TorchGeo''' an OSGeo [https://www.osgeo.org/projects/torchgeo/ Project] [https://github.com/torchgeo/torchgeo CODE] [https://torchgeo.readthedocs.io/ DOCS]

datasets [https://torchgeo.readthedocs.io/en/latest/api/datasets.html LINK], samplers, transforms, and pre-trained models for geospatial data

Additional Topics:

1. '''Workshops'''

Berkeley Climate AI Day [https://qb3.org/recap-symposium-on-ai-and-climate-technology-during-sf-climate-week/ LINK]

'''ml4earth.de''' [https://ml4earth.de/workshop_2025/ workshop] featured a keynote by Prof. Dr. Xiaoxiang Zhu from TUM Germany, also home to the creator of TorchGeo Adam Stewart PhD [https://www.asg.ed.tum.de/sipeo/team/dr-adam-j-stewart/ POSTDOC]. Applications of artificial intelligence (AI) in Earth observation, with a focus on machine learning (ML) approaches for remote sensing.

TerraBytes Canada [https://terrabytes-workshop.github.io/ LINK]

Geometa Lab [https://www.giswiki.ch/Agenda LINK]

2. '''PANGAEA project'''

[[File:Pangaea geofmbenchmark.png|thumb]]

a standardized evaluation protocol that covers a diverse set of datasets, tasks, resolutions, sensor modalities, and temporalities, establishing a robust and widely applicable benchmark for Geospatial Foundation Models (GFMs). [https://arxiv.org/abs/2412.04204v2 PDF]

https://bpa.st/BTQAE ## code updates

https://eotdl.com/blog/pangaea

3. '''Machine learning approaches'''

PANGAEA [https://github.com/VMarsocci/pangaea-bench benchmark] shows that specialized, not-CNN and not-ViT , machine learning models can perform better than various Foundation Models for remote sensing landuse / landcover analysis and other specific classification tasks.
Supervised learning, XGBoost / Catboost or various Random Forest learning can perform better than ViT variations, CNN and related approaches on the six key use cases as stated by FAST-EO : weather and climate disaster analysis, methane leak detection, forest above-ground biomass change, soil property estimation, semantic land cover change detection, and monitoring the expansion of mining fields into farmlands.

Thoughts on Geospatial Foundation Models [https://arxiv.org/pdf/2405.04285 PDF] Zhu, Stewart, et al 2024

this [https://huggingface.co/ibm-esa-geospatial/TerraMind-1.0-base TerraMind] mixed model is experimental,

[https://github.com/swiss-territorial-data-lab/proj-vit swiss-territorial-data-lab] [https://huggingface.co/datasets/heig-vd-geo/M3DRS DATA] https://stdl.ch/

A sparse matrix math tutorial [https://iclr-blogposts.github.io/2025/blog/sparse-autodiff/ LINK]

A Geospatial Foundation Model [https://github.com/NASA-IMPACT/Prithvi-EO-2.0 Prithvi-EO-2.0]

Model building libraries [https://github.com/opengeos/geoai/tree/main geoAI] by _giswqs_ Qiusheng Wu, UTenn [https://opengeoai.org/#statement-of-need DOCS]

processsing toolkit [https://github.com/nasa-nccs-hpda/pytorch-caney pytorch-caney] [https://nasa-nccs-hpda.github.io/pytorch-caney/latest/readme.html#objectives DOCS]

MOSAIKS CodeCapsule [https://codeocean.com/capsule/6456296/tree/v2 LINK]

Satlas [https://satlas-pretrain.allen.ai/ LINK] a platform for visualizing and downloading global geospatial data products generated by AI using satellite images. [https://satlas.allen.ai/map DEMO] YNews [https://news.ycombinator.com/item?id=37387556 REVIEW]

DeepLearning and OBIA [https://arxiv.org/pdf/2408.01607? PDF] sponsored by State Key Labs [https://en.wikipedia.org/wiki/State_Key_Laboratories LINK]

GOOGLE_SATELLITE_EMBEDDING_V1 [https://deepmind.google/discover/blog/alphaearth-foundations-helps-map-our-planet-in-unprecedented-detail/ BLOG] [https://medium.com/google-earth/ai-powered-pixels-introducing-googles-satellite-embedding-dataset-31744c1f4650 MED] [https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_SATELLITE_EMBEDDING_V1_ANNUAL LINK] related [https://christopherren.substack.com/p/embedding-fields-forever LINK]

@inproceedings{brown2024learned,
title={Learned embedding fields for multi-source, multi-temporal earth observation imagery},
author={Brown, Christopher and Kazmierski, Michal and Rucklidge, William and Pasquarella, Valerie and Shelhamer, Evan},
booktitle={ICLR Workshop on Machine Learning for Remote Sensing (ML4RS)},
year={2024}
}

4. '''Data availability'''

Promotion of public datasets for Earth observation research.

Global Forest Watch edu [https://glad.umd.edu/projects/global-forest-watch LINK]

US West Coast -- team MSFT, Planet Labs and The Nature Conservancy .. [https://www.microsoft.com/en-us/research/wp-content/uploads/2025/03/Global-Renewables-Watch_Caleb-Robinson_2025.pdf REF] [https://reatlas42216storage.blob.core.windows.net/public/wind_all_2024q2_3_11_2025.gpkg DATA0]

TerraMesh, ESA (European Space Agency) -- [https://openaccess.thecvf.com/content/CVPR2025W/EarthVision/html/Blumenstiel_TerraMesh_A_Planetary_Mosaic_of_Multimodal_Earth_Observation_Data_CVPRW_2025_paper.html TerraMesh] , part of the FAST‑EO project funded by the European Space Agency Φ‑Lab (contract #4000143501/23/I‑DT).

The [https://www.dlr.de/en/eoc/research-transfer/projects-missions/fast-eo FAST-EO] [https://www.fast-eo.eu/ project], officially [https://www.fz-juelich.de/en/ias/jsc/news/news-items/news-flashes/2024/fast-eo-launched launched] on February 5, 2024, is an initiative funded by the European Space Agency ([https://eo4society.esa.int/projects/fast-eo/ ESA]) and led by the German Aerospace Center (DLR).
Its primary goal is to advance AI Foundation Models (FMs) for Earth Observation (EO) by exploring large multimodal foundation models through unsupervised and self-supervised learning to address downstream tasks.
The project involves a consortium of partners including Forschungszentrum Jülich, KP Labs, and IBM Research, who are collaborating on six key use cases: weather and climate disaster analysis, methane leak detection, forest above-ground biomass change, soil property estimation, semantic land cover change detection, and monitoring the expansion of mining fields into farmlands. The project is committed to open science, planning to release model weights, configurations, datasets, and source code under a free and permissive Apache-2 license, accessible via platforms like GitHub, HuggingFace, and the SpatioTemporal Asset Catalog (STAC).

Other public training [https://x-ytong.github.io/project/Five-Billion-Pixels.html DATA] for China interior, by Prof. Dr. Xiou xiang Zhu .. who is very much in Germany and is the boss of the department with the `torchGeo` project founder

gdal containers [https://github.com/OSGeo/gdal/pkgs/container/gdal LINK]

opendatacube containers [https://github.com/opendatacube/datacube-docker LINK]

https://ceos.org/ard/

[https://github.com/DLR-MF-DAS/SSL4EO-S12-v1.1 SSL4EO-S12-v1]

http://dataspace.copernicus.eu/ https://eotdl.com

https://github.com/opendatacube

[https://eumap.readthedocs.io/en/latest/ EUMap] client libraries

Open Earth Monitor [https://cordis.europa.eu/project/id/101059548/results EU] [https://www.youtube.com/@OpenGeoHubFoundation YouTube] Channel

5. '''Geospatial data infrastructure'''

standardized data formats and efficient data access mechanisms such as STAC (SpatioTemporal Asset Catalog) and OSGeoLive

[https://openlandmap.org/ OpenLandMap]

'''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 #bids25 Big Data from Space #osgeo + Pangeo 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.

A Path for Science‑ and Evidence‑based AI [https://understanding-ai-safety.org/ Policy]

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.

2025-09-26T23:52:28Z

Darkblueb:

[[File:yaosgeologo.jpg|thumb]]

'''TorchGeo''' an OSGeo [https://www.osgeo.org/projects/torchgeo/ Project] [https://github.com/torchgeo/torchgeo CODE] [https://torchgeo.readthedocs.io/ DOCS]

Additional Topics:

1. '''Workshops'''

Berkeley Climate AI Day [https://qb3.org/recap-symposium-on-ai-and-climate-technology-during-sf-climate-week/ LINK]

'''ml4earth.de''' [https://ml4earth.de/workshop_2025/ workshop] featured a keynote by Prof. Dr. Xiaoxiang Zhu from TUM Germany, also home to the creator of TorchGeo Adam Stewart PhD [https://www.asg.ed.tum.de/sipeo/team/dr-adam-j-stewart/ POSTDOC]. Applications of artificial intelligence (AI) in Earth observation, with a focus on machine learning (ML) approaches for remote sensing.

2. '''PANGAEA project'''

[[File:Pangaea geofmbenchmark.png|thumb]]

a standardized evaluation protocol that covers a diverse set of datasets, tasks, resolutions, sensor modalities, and temporalities. It establishes a robust and widely applicable benchmark for Geospatial Foundation Models (GFMs).

https://bpa.st/BTQAE ## code updates

https://eotdl.com/blog/pangaea

3. '''Machine learning approaches'''

PANGAEA [https://github.com/VMarsocci/pangaea-bench benchmark] shows that specialized, not-CNN and not-ViT , machine learning models can perform better than various Foundation Models for remote sensing landuse / landcover analysis and other specific classification tasks.
Supervised learning, XGBoost / Catboost or various Random Forest learning can perform better than ViT variations, CNN and related approaches.

this [https://huggingface.co/ibm-esa-geospatial/TerraMind-1.0-base TerraMind] mixed model is experimental,

another [https://github.com/swiss-territorial-data-lab/proj-vit swiss-territorial-data-lab] [https://huggingface.co/datasets/heig-vd-geo/M3DRS DATA]

A sparse matrix math tutorial [https://iclr-blogposts.github.io/2025/blog/sparse-autodiff/ LINK]

4. '''Data availability'''

Promotion of public datasets for Earth observation research.

US West Coast -- team MSFT, Planet Labs and The Nature Conservancy .. [https://www.microsoft.com/en-us/research/wp-content/uploads/2025/03/Global-Renewables-Watch_Caleb-Robinson_2025.pdf REF] [https://reatlas42216storage.blob.core.windows.net/public/wind_all_2024q2_3_11_2025.gpkg DATA0]

TerraMesh, ESA (European Space Agency) -- [https://openaccess.thecvf.com/content/CVPR2025W/EarthVision/html/Blumenstiel_TerraMesh_A_Planetary_Mosaic_of_Multimodal_Earth_Observation_Data_CVPRW_2025_paper.html TerraMesh] , part of the FAST‑EO project funded by the European Space Agency Φ‑Lab (contract #4000143501/23/I‑DT).

The [https://www.dlr.de/en/eoc/research-transfer/projects-missions/fast-eo FAST-EO] [https://www.fast-eo.eu/ project], officially [https://www.fz-juelich.de/en/ias/jsc/news/news-items/news-flashes/2024/fast-eo-launched launched] on February 5, 2024, is an initiative funded by the European Space Agency ([https://eo4society.esa.int/projects/fast-eo/ ESA]) and led by the German Aerospace Center (DLR).
Its primary goal is to advance AI Foundation Models (FMs) for Earth Observation (EO) by exploring large multimodal foundation models through unsupervised and self-supervised learning to address downstream tasks.
The project involves a consortium of partners including Forschungszentrum Jülich, KP Labs, and IBM Research, who are collaborating on six key use cases: weather and climate disaster analysis, methane leak detection, forest above-ground biomass change, soil property estimation, semantic land cover change detection, and monitoring the expansion of mining fields into farmlands. The project is committed to open science, planning to release model weights, configurations, datasets, and source code under a free and permissive Apache-2 license, accessible via platforms like GitHub, HuggingFace, and the SpatioTemporal Asset Catalog (STAC).

Other public training [https://x-ytong.github.io/project/Five-Billion-Pixels.html DATA] for China interior, by Prof. Dr. Xiou xiang Zhu .. who is very much in Germany and is the boss of the department with the `torchGeo` guy

gdal containers [https://github.com/OSGeo/gdal/pkgs/container/gdal LINK]

opendatacube containers [https://github.com/opendatacube/datacube-docker LINK]

https://ceos.org/ard/

[https://github.com/DLR-MF-DAS/SSL4EO-S12-v1.1 SSL4EO-S12-v1]

http://dataspace.copernicus.eu/ https://eotdl.com

5. '''Geospatial data infrastructure'''

standardized data formats and efficient data access mechanisms such as STAC (SpatioTemporal Asset Catalog) and OSGeoLive

'''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.