A new arXiv paper proposes combining semantic geospatial embeddings from Earth-observation foundation models with the spherical operators and Earth-specific geometry used in weather and climate modeling. The authors argue for representations that carry explicit physical types and geometry awareness, rather than purely semantic features, to better serve downstream Earth science tasks.
A new arXiv preprint proposes an API that bridges Earth Observation data cubes, which store imagery as spatio-temporal arrays, and the tabular or tensor formats that machine learning models expect. Built on the openEO standard, the interface aims to replace platform-specific preprocessing steps so the same ML workflow can run across different EO infrastructures. The work targets researchers who want to train and apply models on satellite data without writing custom conversion code for each provider.
Researchers analyzed 1,744 co-registered Landsat 4-9 and Sentinel-2 image chips to study how Robert Smithson's 1970 land artwork Spiral Jetty has repeatedly been submerged and exposed as the Great Salt Lake shrank. The study treats the artwork as a large-scale visual indicator of changing water levels in the lake's north arm. It suggests long-running satellite archives can document climate-driven hydrological shifts at a specific site.