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 southwest 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%.

翻译：准确的土地利用地图从人为利用角度描述区域特征，是土地管理与规划的重要工具。在制作此类地图时，仅使用光学影像仍存在局限性。因此，有必要利用多种异构数据源，这些数据源因自身不完善性或不同规格而携带互补或矛盾信息。本研究比较了两种不同方法，即分类前融合与分类后融合两种数据融合策略，用于结合多种空间数据源进行土地利用分类。这些方法应用于法国西南部热尔省官方土地利用数据。尽管分类前融合未对数据不完善性进行显式建模，但其最终结果最优，总体精度达到97%，宏平均F1分数达到88%。