About

ESA SmartCrop PiSchool autumn 2019

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
• This is a proof of concept. The above mentioned extensions are going to be included in the new Hugin release.

Documentation for Hugin is available at https://hugin-eo.readthedocs.io/

Acknowledgments Hugin project development is supported by the European Space Agency through the ML4EO project.