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. We introduce TorchGeo, a Python library for integrating geospatial data into the PyTorch deep learning ecosystem. TorchGeo provides data loaders for a variety of benchmark datasets, composable datasets for generic geospatial data sources, samplers for geospatial data, and transforms that work with multispectral imagery.

2021: A. Stewart, Caleb Robinson, I. Corley, Anthony Ortiz, J. Ferres, Arindam Banerjee

https://arxiv.org/pdf/2111.08872v4.pdf

Id. d’épisode : 1000590960725

GUID : papersreadai.podbean.com/82f67ac3-47b9-5e6b-b3c9-4bd6c42e90ca

Date de publication : 22/12/2022 à 10:03:57