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

TL;DR
This paper introduces a scalable machine learning pipeline that efficiently detects building footprints in rural historical maps, enabling large-scale analysis of settlement patterns and historical changes.
Contribution
It presents a hierarchical CNN-based approach that significantly improves efficiency in extracting buildings from rural maps, addressing limitations of prior urban-focused methods.
Findings
High accuracy in building detection on historical maps
Reduced computational load compared to traditional methods
Discovery of potentially abandoned settlements from 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…
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