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Accurate land use maps, describing the territory from an anthropicutilisation point of view, are useful tools for land management and planning.To produce them, the use of optical images alone remains limited. It istherefore necessary to make use of several heterogeneous sources, each carryingcomplementary or contradictory information due to their imperfections or theirdifferent specifications. This study compares two different approaches i.e. apre-classification and a post-classification fusion approach for combiningseveral sources of spatial data in the context of land use classification. Theapproaches are applied on authoritative land use data located in the Gersdepartment in the southwest of France. Pre-classification fusion, while notexplicitly modeling imperfections, has the best final results, reaching anoverall accuracy of 97% and a macro-mean F1 score of 88%.

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Comparison of two data fusion approaches for land use classification