arXiv:2608.13856v2 Announce Type: replace-cross
Abstract: Timely urban-canopy information is essential for linking remote sensing with heat, mobility, and neighborhood planning. We developed an optic...
By Mohammadreza Narimani, Shreyan Mitra, Parastoo Farajpoor
arXiv:2607. 05207v1 Announce Type: cross Abstract: Self-supervised learning (SSL) is designed to learn generic, transferable representations rather than representations optimized for a single task.
By Rohita Mocharla, Vishal M. Patel
arXiv:2608. 04792v1 Announce Type: new Abstract: Accurate estimation of Above-Ground Biomass (AGB) from satellite imagery is essential for the large-scale monitoring of carbon stocks, yet it remains a challenging regression task at global scale.
By Ghjulia Sialellia, Linus Scheibenreif, Jan Dirk Wegner, Konrad Schindler
The paper introduces a framework to evaluate AlphaEarth embeddings for crop classification across spatial, temporal, and cross-regional dimensions. It compares a 64‑dimensional embedding space with traditional Sentinel‑2, NDVI, and harmonic features using field data from the US and India, finding that embeddings offer superior distribution‑aware separability and capture multi‑year crop rotation dynamics, while traditional features transfer better across continents. The study highlights a trade‑off between representational richness and cross‑region transferability, suggesting a hybrid approach for large‑scale crop classification.
By Priyanka Goyal, Maitreyee Sar, Adway Mitra, Manjira Sinha
Self-supervised learning (SSL) is designed to learn generic, transferable representations rather than representations optimized for a single task. Most geospatial benchmarks evaluate representations solely through downstream tasks, providing limited insight into the information encoded within the representation itself.
The study evaluates 13 Geospatial Foundation Models (GFMs) for estimating above‑ground biomass (AGB) from satellite imagery, comparing them to a fully supervised state‑of‑the‑art baseline trained on the AGBD dataset. Most GFMs perform worse than the baseline, but TESSERA and AlphaEarth Foundations (AEF) features combined with specific prediction heads achieve superior results, with the best model attaining an RMSE of 50.79 Mg/ha. This AEF‑based model also generalizes better to unseen regions, reduces RMSE by up to 25% in excluded areas, and matches the ESA CCI biomass product when trained on limited regional labels.
By Ghjulia Sialelli, Linus Scheibenreif, Jan Dirk Wegner, Konrad Schindler