Comparison of two data fusion approaches for land use classification

ABSTRACT: Accurate land use maps, describing the territory from an anthropic utilisation point of view, are useful tools for land management and planning. To produce them, the use of optical images alone remains limited. It is therefore necessary to make use of several heterogeneous sources, each carrying complementary or contradictory information due to their imperfections or their different specifications. This study compares two different approaches i.e. a pre-classification and a post-classification fusion approach for combining several sources of spatial data in the context of land use classification. The approaches are applied on authoritative land use data located in the Gers department in the south-west of France. Pre-classification fusion, while not explicitly modeling imperfections, has the best final results, reaching an overall accuracy of 97% and a macro-mean F1 score of 88%. 1. INTRODUCTION At the feature level, Fonte et al. (2018) identified building functions using Land Use (LU) describes the socio-economic human activity of a rule based classifications of OpenStreetMap (OSM), Facebook an area (e.g. Land al. (2022) identified building functions from images, POI and Use and Land Cover (LULC) maps are very useful for understanding, building footprint from Gaode map (authoritative database) and monitoring, planning and predicting the evolution of distance to OSM roads using a XGBoost classifier.