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TorchGeo is a PyTorch library designed for deep learning on geospatial data, providing a framework for building and training neural networks for tasks such as semantic segmentation, object detection, and change detection. It serves as a comprehensive pipeline for remote sensing, featuring specialized dataset loaders and multispectral image preprocessing tools. The library is distinguished by a dedicated remote sensing model zoo and extensive support for transfer learning, allowing users to integrate pre-trained weights optimized for specific satellite sensors. It also includes support for sel
This example demonstrates a few ways to get data into your binder.
Easily access and explore the SAR data products of the Copernicus Sentinel-1 satellite mission in Python.
Code for 3rd place solution in Kaggle Understanding Clouds from Satellite Images Challenge.
The main features of naivelamb/kaggle-cloud-organization are: Satellite Datasets.
Projects with overlapping indexed features include: torchgeo/torchgeo — TorchGeo is a PyTorch library designed for deep learning on geospatial data, providing a framework for building and… binder-examples/getting-data — This example demonstrates a few ways to get data into your binder. bopen/xarray-sentinel — Easily access and explore the SAR data products of the Copernicus Sentinel-1 satellite mission in Python. chrieke/awesome-satellite-imagery-datasets — 🛰️ List of satellite image training datasets with annotations for computer vision and deep learning. ckyrkou/emergencynet — There is a need to design specialized networks that are inherently computationally efficient to enable there use in… acgeospatial/sentinel-5p — Sentinel5P_Python.
