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Orthophoto segmentation\n\n[![Actions Status](https://github.com/orthoseg/orthoseg/actions/workflows/tests.yml/badge.svg?branch=main)](https://github.com/orthoseg/orthoseg/actions?query=workflow%3ATests)\n[![Coverage Status](https://codecov.io/gh/orthoseg/orthoseg/branch/main/graph/badge.svg)](https://codecov.io/gh/orthoseg/orthoseg)\n[![PyPI version](https://img.shields.io/pypi/v/orthoseg.svg)](https://pypi.org/project/orthoseg)\n[![DOI](https://zenodo.org/badge/147507046.svg)](https://zenodo.org/doi/10.5281/zenodo.10340584)\n\nA python package that makes it (relatively) easy to segment orthophotos. Any type of\ngeoreferenced images should work, e.g. satellite, aerial or drone images, (historical)\nmaps, hillshades,...\n\nNo programming is needed, everything is managed via configuration files.\n\nThe typical steps:\n1. create a training dataset for a topic of your choice, e.g. in QGIS\n2. train a neural network to segment orthophotos\n3. run the segmentation on a larger area + vectorize the result\n4. apply some basic postprocessing like dissolve, simplify,...\n\nOnly open source software is needed, eg. QGIS and tensorflow.\n\nInstallation and usage instructions can be found in the [orthoseg docs](https://orthoseg.readthedocs.io)\n\nThis is an example of how the output of a tree detection on aerial images can look:\n\n![Result of a tree detection on aerial images](docs/_static/images/trees.jpg)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Forthoseg%2Forthoseg","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Forthoseg%2Forthoseg","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Forthoseg%2Forthoseg/lists"}