ArchiveLens extracts detailed building data from historical urban maps
ArchiveLens: A Generative Spatial Knowledge Framework for Semantic Reconstruction of Historical Maps
Artificial IntelligenceComputers and SocietyMachine LearningSocial and Information Networks
Summary
Historical maps show lots of details about old cities but often only their shapes get digitized. The authors created ArchiveLens, a computer system that not only finds building outlines but also reads their materials and uses by combining image analysis and language understanding. This system turns old maps into digital databases where you can search and compare building features over time. They tested it on 445 pages of Chicago maps from 1918 to 1950 and got accurate results. This helps people study how cities changed using detailed map information.
What this means in practice
- •For urban planning teams: Create detailed digital records of past urban environments to guide restoration or development using rich building attribute data across time.
- •For cultural heritage organizations: Build searchable databases from historical maps to document and analyze historical building features for preservation efforts.
Authors
Yanan Wu, Yalin Yang, Chengbin Deng
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.