SmartCrop is build upon the Hugin framework version 0.1.x. Hugin is a Python framework designed to help the scientists run Machine Learning experiments on geospatial raster data.
Current extensions include:
- Z Score standardization performed over entire training set/per channel before training
- Transfer weights without including last classsification layer
- Tiling the image without the requirement of having all the input images of the same size
- Include U-Net model topology
- Include a proposed implementation for HSN model and W-Net model
- Include Hugin configuration files for both training and prediction phases for U-Net, HSN and W-Net
- Add prediction metric computation for multi-class semantic segmentation
This is a proof of concept. The above mentioned extensions are going to be included in the new Hugin release. Please visit the Wiki page for a detailed information about the content of this repository.
Additional documentation for Hugin is available at https://hugin-eo.readthedocs.io/
This project was carried out under the supervision of the following stakeholders ESA, E-Geos, UrbyetOrbit, MEEO (Italy) and SISTEMA (Austria) .
Hugin project development is supported by the European Space Agency through the ML4EO project.