Mapping urban deprivation from Sentinel 1/2 using artificial intelligence and weakly labelled data

This research aims at assessing the potential of an semi-unsupervised approach to create labeled samples for predicting deprivation probability at 100x100m. We compare Machine Learning (ML) and Deep Learning approaches (DL) with a combination of Sentinel-1 (S1) and Sentinel-2 (S2) data. Our results confirm that the propose approach for creating labelled samples using semi-supervised method works. The best performance is achieve by the ML approach combining S2 and S1 data and reach an overall accuracy of 95.34%.