Accurate land-cover mapping in tropical regions remains challenging because of high environmental heterogeneity, complex vegetation structure, and persistent cloud cover. Recent geospatial foundation models provide pre-computed Geo-embeddings that offer a new representation of Earth Observation data (EO), yet their pot...
Accurate forest-cover mapping is essential for large-scale resource monitoring; however, acquiring dense pixel-level labels remains costly and limits practical scalability. This study investigates the use of publicly available land-use/land-cover products (PLCPs) as supervisory signals to alleviate the scarcity of high...
Accurate urban land cover information is essential for monitoring urbanization and environmental change, yet the complexity and heterogeneity of urban landscapes remain challenging for high-resolution remote sensing classification. This study used Google Satellite Embeddings as the core feature input, combined with ens...
The results support frozen geospatial embeddings as a low-compute candidate for regional cropland mapping, subject to the limits of a single-state study, a 30 m-derived training reference, and a one-block human validation.
Spectral remote sensing struggles in hyper-arid environments because desert substrates share overlapping optical signatures. We evaluate AlphaEarth foundation-model embeddings—64-dimensional annual representations fused from Sentinel-1, Sentinel-2, Landsat, and LiDAR—for land-cover classification and change detection a...
The resulting map, released as an open-access product, is the first high-resolution national tree species map of Denmark and provides a valuable resource for forest monitoring, ecological research, and land management applications.
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