Abstract
AbstractExtracting features from printed maps has been a challenge for decades; historical maps pose an even larger problem due to manual, inconsistent drawing or scribing, low printing quality, and geometrical distortions. In this article, a new workflow is introduced, consisting of a segmentation step and a vectorization step to acquire high‐quality polygon representations of building footprints from the Siegfried map series. For segmentation, an ensemble of U‐Nets is trained, yielding pixel‐based predictions with an average intersection over union of 88.2% and an average precision of 98.55%. For vectorization, methods based on contour tracing and orientation‐based clustering are proposed to approximate idealized polygonal representations. The workflow has been tested on 10 randomly selected map sheets from the Siegfried map, showing that the time required to manually correct these polygons drops to about 45 min per map sheet. Of this sample, approximately 10% of buildings required manual corrections. This workflow can serve as a blueprint for similar vectorization efforts.
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