Georeferencing Historical Maps at Scale

Abstract

This paper presents a novel approach to automatically georeferencing historical maps using an algorithm based on salient line intersections. Our algorithm addresses the challenges inherent in linking historical map images to contemporary cadastral data, particularly those due to temporal discrepancies, cartographic distortions, and map image noise. By extracting and comparing angular relationships between cadastral features, termed monads and dyads, we establish a robust method for performing record linkage by identifying corresponding spatial patterns across disparate datasets. We employ a Bayesian framework to quantify the likelihood of dyad matches corrupted by measurement noise. The algorithm’s performance was evaluated by selecting a map image and finding putative angle correspondences from the entirety of Aotearoa New Zealand. Even when restricted to a single dyad match, >99% of candidate regions can be successfully filtered out. We discuss the implications and limitations, and suggest strategies for further enhancing the algorithm’s robustness and efficiency. Our work is motivated by previous work in the areas of critical GIS, critical cartography and spatial justice and seeks to contribute to the areas of Spatial Data Science, Historical GIS and GIScience.