Towards the automated large-scale reconstruction of past road networks from historical maps

TL;DR

This paper presents a framework for reconstructing historical road networks from scanned maps by integrating contemporary data and image processing, enabling long-term urbanization analysis with high accuracy.

Contribution

The authors develop a novel method combining image analysis and clustering to extract historical roads from maps, validated on large datasets with high accuracy.

Findings

Achieved F-1 scores up to 0.95 in network extraction

Reconstructed over 50,000 km of roads from 1890-1950 maps

Demonstrated plausibility of long-term urbanization patterns

Abstract

Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization. For sustainable planning and informed decision making, a thorough understanding of the long-term evolution of transportation infrastructure such as road networks is crucial. However, spatially explicit, multi-temporal road network data covering large spatial extents are scarce and rarely available prior to the 2000s. Herein, we propose a framework that employs increasingly available scanned and georeferenced historical map series to reconstruct past road networks, by integrating abundant, contemporary road network data and color information extracted from historical maps. Specifically, our method uses contemporary road segments as analytical units and extracts historical roads by inferring their existence in historical map series based on image processing and clustering techniques. We tested our method on over 300,000 road segments representing more than 50,000 km of the road network in the United States, extending across three study areas that cover 53 historical topographic map sheets dated between 1890 and 1950. We evaluated our approach by comparison to other historical datasets and against manually created reference data, achieving F-1 scores of up to 0.95, and showed that the extracted road network statistics are highly plausible over time, i.e., following general growth patterns. We demonstrated that contemporary geospatial data integrated with information extracted from historical map series open up new avenues for the quantitative analysis of long-term urbanization processes and landscape changes far beyond the era of operational remote sensing and digital cartography.