Neural networks for semantic segmentation of historical city maps: Cross-cultural performance and the impact of figurative diversity
This addresses the challenge of inflexible algorithms in automatic map processing for researchers and historians, though it appears incremental as it builds on existing neural network methods.
The paper tackles the problem of semantic segmentation for historical city maps by developing a neural network model that surpasses state-of-the-art in flexibility and performance, achieving efficient segmentation across maps with high figurative diversity.
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.