A Scalable Machine Learning Pipeline for Building Footprint Detection in Historical Maps

Abstract

Historical maps offer a valuable lens through which to study past landscapes and settlement patterns. While prior research has leveraged machine learning based techniques to extract building footprints from historical maps, such approaches have largely focused on urban areas and tend to be computationally intensive. This presents a challenge for research questions requiring analysis across extensive rural regions, such as verifying historical census data or locating abandoned settlements. In this paper, this limitation is addressed by proposing a scalable and efficient pipeline tailored to rural maps with sparse building distributions. The method described employs a hierarchical machine learning based approach: convolutional neural network (CNN) classifiers are first used to progressively filter out map sections unlikely to contain buildings, significantly reducing the area requiring detailed analysis. The remaining high probability sections are then processed using CNN segmentation algorithms to extract building features. The pipeline is validated using test sections from the Ordnance Survey Ireland historical 25 inch map series and 6 inch map series, demonstrating both high performance and improved efficiency compared to conventional segmentation-only approaches. Application of the technique to both map series, covering the same geographic region, highlights its potential for historical and archaeological discovery. Notably, the pipeline identified a settlement of approximately 22 buildings in Tully, Co. Galway, present in the 6 inch map, produced in 1839, but absent from the 25 inch map, produced in 1899, suggesting it may have been abandoned during the Great Famine period.