用深度学习自动分析历史地图，量化城市变迁
Deep learning enables urban change profiling through alignment of historical maps
- 通过深度学习实现历史地图的精准对齐与物体检测
- 在1868-1937年巴黎地图中发现城市扩张的时空差异
- 适合研究城市史、社会学及文化遗产的学者使用
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，是理解城市身份演变的重要视角。然而，由于空间错位、制图差异和纸张退化，从历史地图系列中提取一致且细粒度的变化信息仍具挑战性，多数分析局限于小范围或定性方法。本文提出一种全自动化、基于深度学习的框架，用于大规模历史地图的精细城市变迁分析，其模块化设计融合了密集地图对齐、多时相目标检测与变化画像。该框架将历史地图分析从随意视觉比对转变为系统、定量的城市变迁刻画。实验表明，所提对齐与目标检测方法具有鲁棒性。应用于1868至1937年间的巴黎地图，揭示了城市转型在空间与时间上的异质性，对社会科学与人文学科研究具有重要意义。框架的模块化设计也支持适配多种制图背景与下游应用。
原文摘要 · Abstract (English)
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.
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