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The main features of kgpml/hyperspectral are: Geospatial Machine Learning, Specialized Segmentation.
Projects with overlapping indexed features include: mapbox/robosat — Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads,… azavea/raster-vision. torchgeo/torchgeo — TorchGeo is a PyTorch library designed for deep learning on geospatial data, providing a framework for building and… avanetten/simrdwn — The Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN) codebase combines some of the… ayoolaolafenwa/pixellib — Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/. avanetten/yolt — You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery.
Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds
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
The Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN) codebase combines some of the leading object detection algorithms into a unified framework designed to detect objects both large and small in overhead imagery. This work seeks to extend the YOLT modification of…