PolyMapper: Extracting City Maps using Polygons
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We propose a method to leapfrog pixel-wise, semantic segmentation of (aerial) images and predict objects in a vector representation directly. PolyMapper predicts maps of cities from aerial images as collections of polygons with a learnable framework. Instead of the usual multi-step procedure of semantic segmentation, shape improvement, conversion to polygons, and polygon refinement, our approach learns mappings with a single network architecture and directly outputs maps. We demonstrate that our method is capable of drawing polygons of buildings and road networks that very closely approximate the structure of existing online maps such as OpenStreetMap, and it does so in a fully automated manner. Validation on existing and novel large scale datasets of several cities show that our approach achieves good levels of performance.
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