Deep learning enables urban change profiling through alignment of historical maps

TL;DR

This paper introduces a deep learning framework for automated, fine-grained analysis of urban change using historical maps, overcoming challenges of misalignment and degradation to enable systematic urban transformation studies.

Contribution

It presents a novel, modular deep learning approach for aligning, detecting, and profiling urban changes from historical maps, advancing beyond qualitative methods.

Findings

Robust map alignment and object detection performance

Revealed spatial and temporal heterogeneity in Paris urban change

Framework adaptable to diverse cartographic contexts

Abstract

Prior to modern Earth observation technologies, historical maps provide a unique record of long-term urban transformation and offer a lens on the evolving identity of cities. However, extracting consistent and fine-grained change information from historical map series remains challenging due to spatial misalignment, cartographic variation, and degrading document quality, limiting most analyses to small-scale or qualitative approaches. We propose a fully automated, deep learning-based framework for fine-grained urban change analysis from large collections of historical maps, built on a modular design that integrates dense map alignment, multi-temporal object detection, and change profiling. This framework shifts the analysis of historical maps from ad hoc visual comparison toward systematic, quantitative characterization of urban change. Experiments demonstrate the robust performance of the proposed alignment and object detection methods. Applied to Paris between 1868 and 1937, the framework reveals the spatial and temporal heterogeneity in urban transformation, highlighting its relevance for research in the social sciences and humanities. The modular design of our framework further supports adaptation to diverse cartographic contexts and downstream applications.