U-Net semantic segmentation for satellite imagery
This digital tool is part of the catalog of tools of the Inter-American Development Bank. You can learn more about the IDB initiative at code.iadb.org
A set of classes and CLI tools for training a semantic segmentation model based on the U-Net architecture, using Tensorflow and Keras.
This implementation is tuned specifically for satellite imagery and other geospatial raster data.
Bug reports and pull requests are welcome on GitHub at the issues page. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.
Made with contrib.rocks.
The current roadmap is available at GitHub at the projects page.
This project is licensed under Apache 2.0. Refer to LICENSE.txt.