Sprint bdfs25

TorchGeo an OSGeo Project CODE DOCS

datasets LINK, samplers, transforms, and pre-trained models for geospatial data

Additional Topics:

1. Workshops

Berkeley Climate AI Day LINK

ml4earth.de workshop featured a keynote by Prof. Dr. Xiaoxiang Zhu from TUM Germany, also home to the creator of TorchGeo Adam Stewart PhD POSTDOC. Applications of artificial intelligence (AI) in Earth observation, with a focus on machine learning (ML) approaches for remote sensing.

TerraBytes Canada LINK

2. PANGAEA project

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). PDF

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

https://eotdl.com/blog/pangaea

3. Machine learning approaches

PANGAEA 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 TerraMind mixed model is experimental,

swiss-territorial-data-lab DATA https://stdl.ch/

A sparse matrix math tutorial LINK

A Geospatial Foundation Model Prithvi-EO-2.0

Model building libraries geoAI by _giswqs_ Qiusheng Wu, UTenn DOCS

processsing toolkit pytorch-caney DOCS

MOSAIKS CodeCapsule LINK

Satlas LINK a platform for visualizing and downloading global geospatial data products generated by AI using satellite images. DEMO YNews REVIEW

4. Data availability

Promotion of public datasets for Earth observation research.

US West Coast -- team MSFT, Planet Labs and The Nature Conservancy .. REF DATA0

TerraMesh, ESA (European Space Agency) -- TerraMesh , part of the FAST‑EO project funded by the European Space Agency Φ‑Lab (contract #4000143501/23/I‑DT).

The FAST-EO project, officially launched on February 5, 2024, is an initiative funded by the European Space Agency (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 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 LINK

opendatacube containers LINK

https://ceos.org/ard/

SSL4EO-S12-v1

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

https://github.com/opendatacube

EUMap client libraries

Open Earth Monitor EU YouTube Channel

5. Geospatial data infrastructure

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

OpenLandMap

Thoughts on Geospatial Foundation Models PDF Zhu, Stewart, et al 2024

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.

A Path for Science‑ and Evidence‑based AI 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.