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TorchGeo: Deep Learning With Geospatial Data
[article]
2022
arXiv
pre-print
Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available. However, the variance in data collection methods and handling of geospatial metadata make
arXiv:2111.08872v4
fatcat:lxfujweoefdbrcs3c674ogj6k4