Cropland Mapping using Geospatial Embeddings
This work addresses the need for accurate land cover maps for climate change analysis, but it is incremental as it applies existing embedding methods to a new dataset.
The study tackled cropland mapping in Togo using geospatial embeddings from Presto and AlphaEarth, achieving high-accuracy classification to support land use change assessments.
Accurate and up-to-date land cover maps are essential for understanding land use change, a key driver of climate change. Geospatial embeddings offer a more efficient and accessible way to map landscape features, yet their use in real-world mapping applications remains underexplored. In this work, we evaluated the utility of geospatial embeddings for cropland mapping in Togo. We produced cropland maps using embeddings from Presto and AlphaEarth. Our findings show that geospatial embeddings can simplify workflows, achieve high-accuracy cropland classification and ultimately support better assessments of land use change and its climate impacts.