ArchiveLens：面向历史地图语义重建的生成式空间知识框架
ArchiveLens: A Generative Spatial Knowledge Framework for Semantic Reconstruction of Historical Maps
AI 导读
新提出的 ArchiveLens 框架将历史地图转化为以实体为中心、可机器查询的地理空间数据库。该框架分三阶段：YOLOv11-seg 实例分割提取建筑轮廓、基于 HSV 颜色阈值的材料分类、量化视觉语言模型解析局部属性，并集成结合稠密语义检索与符号时空过滤的 ST-RAG 架构。应用于芝加哥 1918-1950 年间 445 张 Sanborn 地图，实现了较高的几何与语义精度。
正文 · 原文
Abstract:Historical maps contain dense morphological and semantic information about past urban environments, but many digitization workflows focus primarily on extracting geometry, and do not capture cartographic semantics such as building use and construction materials. This study introduces ArchiveLens, a generative spatial knowledge framework that operationalizes historical maps as entity-centric, machine-queryable geospatial databases. The pipeline decouples morphological and semantic reconstruction through three stages: tiled YOLOv11-seg instance segmentation for building footprint extraction; deterministic material classification via hue, saturation, and value (HSV)-based color thresholds; and localized attribute parsing using a quantized vision-language model constrained by domain-specific prompts. Extracted entities are integrated into a hybrid Spatial-Temporal Retrieval-Augmented Generation (ST-RAG) architecture that combines dense semantic retrieval with symbolic spatio-temporal filtering and mandatory provenance grounding. Applied to 445 georeferenced Sanborn map sheets of Chicago (1918-1950), the framework achieves high geometric and semantic accuracy. Beyond performance, ArchiveLens advances GIScience by formalizing historical cartography as structured spatial knowledge representation, allowing researchers to query building attributes and compare spatial patterns across map sheets and years.
| Comments: | 31 pages |
| Subjects: | Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG); Social and Information Networks (cs.SI) |
| Cite as: | arXiv:2610.35606 [cs.AI] |
| (or arXiv:2610.35606v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35606 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yanan Wu [view email]
[v1]
Mon, 26 Oct 2026 15:29:24 UTC (2,477 KB)
来源：arXiv cs.AI · arxiv.org