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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
Images to inference with no labeling (use foundation models to train supervised models).
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…
LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)
Data Preparation for Satellite Machine Learning
The main features of developmentseed/label-maker are: Geospatial Machine Learning, Image Annotation Tools.
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… autodistill/autodistill — Images to inference with no labeling (use foundation models to train supervised models). avanetten/simrdwn — The Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN) codebase combines some of the… avanetten/yolt — You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery. ayoolaolafenwa/pixellib — Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/. ansleliu/lightnet — LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset).