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Microsoft Maps is releasing country wide open building footprints datasets in United States. This dataset contains 129,591,852 computer generated building footprints derived using our computer vision algorithms on satellite imagery. This data is freely available for download and use.
The main features of microsoft/usbuildingfootprints are: Instance Segmentation, 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.
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.
