Petitpierre, Rémi GuillaumeKaplan, FrédéricDi Lenardo, Isabella2022-11-182022-11-182022-11-182021-11-17https://infoscience.epfl.ch/handle/20.500.14299/192315Research in automatic map processing is largely focused on homogeneous corpora or even individual maps, leading to inflexible models. Based on two new corpora, the first one centered on maps of Paris and the second one gathering maps of cities from all over the world, we present a method for computing the figurative diversity of cartographic collections. In a second step, we discuss the actual opportunities for CNN-based semantic segmentation of historical city maps. Through several experiments, we analyze the impact of figurative and cultural diversity on the segmentation performance. Finally, we highlight the potential for large-scale and generic algorithms. Training data and code of the described algorithms are made open-source and published with this article.historical map processingneural networkssemantic segmentationcomputer visiontopologytext::conference output::conference proceedings::conference paper