Deep learning enables urban change profiling through alignment of historical maps
This work addresses the problem of analyzing long-term urban change for researchers in social sciences and humanities by providing a systematic, quantitative tool, though it is incremental as it builds on existing deep learning methods for alignment and detection.
The researchers tackled the challenge of extracting consistent and fine-grained urban change information from historical maps by developing a fully automated deep learning framework that integrates dense map alignment and multi-temporal object detection, enabling systematic quantitative analysis of urban transformation, as demonstrated with data from Paris between 1868 and 1937.
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.