  In this work, we present a new semantic segmentation model for historical
city maps that surpasses the state of the art in terms of flexibility and
performance. Research in automatic map processing is largely focused on
homogeneous corpora or even individual maps, leading to inflexible algorithms.
Recently, convolutional neural networks have opened new perspectives for the
development of more generic tools. Based on two new maps corpora, the first one
centered on Paris and the second one gathering cities from all over the world,
we propose a method for operationalizing the figuration based on traditional
computer vision algorithms that allows large-scale quantitative analysis. In a
second step, we propose a semantic segmentation model based on neural networks
and implement several improvements. Finally, we analyze the impact of map
figuration on segmentation performance and evaluate future ways to improve the
representational flexibility of neural networks. To conclude, we show that
these networks are able to semantically segment map data of a very large
figurative diversity with efficiency.
