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azavea/raster-vision

0
View on GitHub↗
2,204 stars·395 forks·Python·apache-2.0·10 viewsdocs.rastervision.io↗

Raster Vision

Features

  • Geospatial Machine Learning - Frameworks for deep learning on satellite and aerial imagery.
  • Specialized Segmentation - Framework for satellite imagery analysis.
  • Remote Sensing and Imagery - Deep learning framework for aerial and satellite imagery.

Star history

Star history chart for azavea/raster-visionStar history chart for azavea/raster-vision

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Open-source alternatives to Raster Vision

Similar open-source projects, ranked by how many features they share with Raster Vision.
  • kgpml/hyperspectralK

    KGPML/Hyperspectral

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  • mapbox/robosatmapbox avatar

    mapbox/robosat

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    Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds

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  • google/earthengine-apigoogle avatar

    google/earthengine-api

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    The Google Earth Engine Client Library is a programmatic interface for accessing, manipulating, and exporting planetary-scale raster and vector data via cloud-based services. It serves as a satellite imagery processing API that allows for the filtering, composing, and manipulation of multi-spectral image collections from a remote geospatial catalog. The system functions as a cloud-based geospatial analysis tool, performing server-side computations such as linear fits and trend analysis. It includes an asynchronous geospatial export utility to trigger batch jobs that save processed images and

    JavaScript
    View on GitHub↗3,207
  • torchgeo/torchgeotorchgeo avatar

    torchgeo/torchgeo

    4,077View on GitHub↗

    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

    Pythoncomputer-visiondatasetsdeep-learning
    View on GitHub↗4,077
See all 30 alternatives to Raster Vision→

Frequently asked questions

What are the main features of azavea/raster-vision?

The main features of azavea/raster-vision are: Geospatial Machine Learning, Specialized Segmentation, Remote Sensing and Imagery.

What are some open-source alternatives to azavea/raster-vision?

Open-source alternatives to azavea/raster-vision include: kgpml/hyperspectral. mapbox/robosat — Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads,… torchgeo/torchgeo — TorchGeo is a PyTorch library designed for deep learning on geospatial data, providing a framework for building and… google/earthengine-api — The Google Earth Engine Client Library is a programmatic interface for accessing, manipulating, and exporting… charlotte-pel/temporalcnn. avanetten/simrdwn — The Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN) codebase combines some of the…