Torchgeo: deep learning with geospatial data

AJ Stewart, C Robinson, IA Corley, A Ortiz… - Proceedings of the 30th …, 2022 - dl.acm.org
Remotely sensed geospatial data are critical for applications including precision agriculture,
urban planning, disaster monitoring and response, and climate change research, among …

[HTML][HTML] FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery

LB Ferreira, VS Martins, URV Aires… - … and Electronics in …, 2025 - Elsevier
Accurate delineation of agricultural fields from satellite imagery is crucial for digital
agriculture and conservation. The Segment Anything Model (SAM), a state-of-the-art image …

A region-wide, multi-year set of crop field boundary labels for Africa

LD Estes, A Wussah, M Asipunu, M Gathigi… - arxiv preprint arxiv …, 2024 - arxiv.org
African agriculture is undergoing rapid transformation. Annual maps of crop fields are key to
understanding the nature of this transformation, but such maps are currently lacking and …