The paper evaluates the Quebec-specific 10‑m land‑cover product COTQ against three global 10‑m datasets (ESA WorldCover, ESRI LandCover, and Google DynamicWorld). Using structural indicators, spectral separability metrics, and photo‑interpretation, the study finds that COTQ most closely resembles ESA WorldCover but shows systematic differences in urban, wetland, and rocky classes. The analysis clarifies COTQ’s relative strengths and weaknesses for operational land monitoring in Quebec.
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...
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.
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.
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.
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.
The paper introduces a multi‑sensor deep learning framework for mapping informal settlements in Córdoba, Argentina, using high‑resolution PlanetScope multispectral imagery, COSMO‑SkyMed SAR data, and medium‑resolution PRISMA hyperspectral observations. It compares SAR‑only, MS‑only, and various fusion strategies (early, middle, late) and finds that late fusion with hyperspectral data (LF+HS) delivers the best balance of classification accuracy and spatial precision. The study also demonstrates that detections outside official polygons align with broader municipal vulnerability layers and that identified settlements show higher surface temperatures during a heatwave, highlighting localized heat amplification.
arXiv:2608. 12001v1 Announce Type: cross Abstract: Rapid urbanization in Dhaka District, Bangladesh has triggered substantial alterations in land use and environmental conditions, necessitating systematic monitoring for informed urban planning and ecological sustainability.
arXiv:2607. 27217v1 Announce Type: cross Abstract: Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage, yet regional carbon monitoring remains constrained by the sparse spatial and temporal availability of field inventories and airborne structural measurements.
The study maps tree species across Denmark using National Forest Inventory plots and Earth Observation data, comparing manually engineered spectral‑temporal features (STF) from Sentinel‑1 and Sentinel‑2 with embeddings from the foundation models TESSERA and AlphaEarth. Random forest, XGBoost, and MLP classifiers were evaluated, with the STF‑based MLP achieving the highest macro F1 scores for pure and mixed stands. The best model was applied nationally to produce a 10 m resolution tree species map, achieving 79.9% area‑adjusted accuracy and released as an open‑access product.
arXiv:2609.13182v1 Announce Type: new Abstract: Robert Smithson's 1970 land artwork Spiral Jetty, located in the north arm of Utah's Great Salt Lake, has alternated between submergence and exposure d...
FLORO is a multimodal geospatial foundation model that learns transferable representations from a small but diverse remote sensing corpus, including Sentinel-1, Sentinel-2, SkySAT imagery, elevation, and UAV data. It uses masked autoencoding and availability‑aware inputs to handle varying sensor configurations, enabling a unified input space across heterogeneous modalities. Evaluated on the PANGAEA benchmark, FLORO achieved strong, stable transfer performance across scene classification, segmentation, and regression tasks, ranking second in segmentation and third in scene classification while excelling in regression tasks.