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.

翻译：在现代地球观测技术出现之前，历史地图为长期城市变迁提供了独特记录，并为城市不断演变的身份特征提供了观察视角。然而，由于空间错位、制图差异和文档质量退化等问题，从历史地图系列中提取一致且细粒度的变化信息仍然具有挑战性，这导致大多数分析仅限于小规模或定性方法。我们提出了一种基于深度学习的全自动框架，用于从大规模历史地图收藏中进行细粒度城市变化分析。该框架采用模块化设计，集成了密集地图对齐、多时相目标检测和变化剖析功能。该框架将历史地图分析从临时性的视觉比较转向系统化、定量化的城市变化表征。实验表明，所提出的对齐与目标检测方法具有鲁棒性能。将该框架应用于1868年至1937年间的巴黎，揭示了城市转型的时空异质性，凸显了其在社会科学与人文科学研究中的价值。我们框架的模块化设计进一步支持其适应多样化的制图背景与下游应用。