Exploring inference of a land use and land cover model trained on MultiSenGE dataset

Abstract

Land use and land cover and Urban Fabric (UF) mapping are very useful for urban modeling and simulation (growth, pollution, noise, micro-climate, mobility) in a context of global change. In recent years, due to the increase of Earth Observation data researchers built and shared datasets to the machine learning scientific community to apply and test their models for semantic segmentation. Few works have trained their deep learning model based on a geographic region and applied it to another geographical area of the same country. In this study, we explore the inference of a deep learning model pretrained on a multitemporal and multimodal dataset named 'MultiSenGE' dataset (based on a region in the East of France representing one fifth of the area of France) on five different cities over France (Toulouse, Dijon, Orléans, Lille, Rennes) away from the trained area. Results are encouraging and achieve F1-Score between 0.70 and 0.80 for the five urban fabric classes.