Neural networks for semantic segmentation of historical city maps: Cross-cultural performance and the impact of figurative diversity

TL;DR

This paper introduces a new neural network-based semantic segmentation model for historical city maps, demonstrating its ability to handle diverse figurative styles across different cultures with improved flexibility and performance.

Contribution

The paper presents a novel neural network model tailored for diverse historical maps and introduces a method to analyze figurative diversity's impact on segmentation performance.

Findings

Neural networks effectively segment maps with high figurative diversity.

The proposed method outperforms previous state-of-the-art models.

Figurative diversity significantly influences segmentation accuracy.

Abstract

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.