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

par Govoorts, Julien;Grippa, Taïs ;Vanhuysse, Sabine ;Wolff, Eléonore
Référence 2023 Joint Urban Remote Sensing Event (JURSE)(2023: Heraklon, Greece), 2023 Joint Urban Remote Sensing Event (JURSE)
Publication Publié, 2023-05

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Résumé :

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%.