深度学习通过历史地图的对齐实现城市变化分析
Deep learning enables urban change profiling through alignment of historical maps
AI总结：
本文提出基于深度学习的框架，通过历史地图的对齐实现城市变化的系统性分析，揭示城市变迁的空间和时间异质性，适用于社会科学和人文学科研究。
摘要：
在现代地球观测技术之前，历史地图提供了长期城市变迁的独特记录，并为城市演变身份提供了观察窗口。然而，从历史地图系列中提取一致且细致的变化信息仍然具有挑战性，因为存在空间错位、制图差异和文档质量下降等问题，限制了大多数分析仅限于小规模或定性方法。我们提出了一种完全自动化、基于深度学习的框架，用于从大量历史地图中进行细致的城市变化分析，该框架基于模块化设计，集成了密集地图对齐、多时相目标检测和变化分析。该框架将历史地图的分析从随意的视觉比较转向系统性的城市变化定量描述。实验表明所提出的对齐和目标检测方法具有稳健的性能。应用于1868至1937年的巴黎，该框架揭示了城市变迁在空间和时间上的异质性，突显了其在社会科学和人文学科研究中的相关性。我们框架的模块化设计进一步支持适应不同的制图环境和下游应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
