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Torchgeo: deep learning with geospatial data
Remotely sensed geospatial data are critical for applications including precision agriculture,
urban planning, disaster monitoring and response, and climate change research, among …
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
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 …
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 …
understanding the nature of this transformation, but such maps are currently lacking and …