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Historical OpenStreetMap Objects to Machine Learning Training Samples

Project description


Historical OpenStreetMap Objects to Machine Learning Training Samples

The ohsome2label offers a flexible label preparation tool for satellite machine learning applications.

  • Customized Object - user-defined geospatial objects are retrieved and extracted from OpenStreetMap full-history data by requesting ohsome web API.
  • Various Satellite Images - user could downloads corresponding satellite imagery tiles from different data providers.
  • Seamless Training - object labels together with images would be packaged and converted to Microsoft COCO .json format to provide a seamleass connection to further model training.

The output package could support directly training of popular machine learning tasks (e.g., object detection, semantic segmentation, instance segmentation etc,).

Package Dependencies

  • python 3.6


pip install ohsome2label


The package relies heavily on the OpenStreetMap History Data Analysis Framework under the ohsome API. The idea of this package has been inspired by the excellent work of label-maker. Last but not lease, we would like to thanks for the contributions of OpenStreetMap volunteer to make this happen.

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Files for ohsome2label, version 1.1.2
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