Polygonal Building Segmentation by Frame Field Learning
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While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. We propose adding a frame field output to a deep image segmentation model for extracting buildings from remote sensing images. This improves segmentation quality and provides structural information, facilitating more accurate polygonization. To this end, we train a deep neural network, which aligns a predicted frame field to ground truth contour data. In addition to increasing performance by leveraging multi-task learning, our method produces more regular segmentations. We also introduce a new polygonization algorithm, which is guided by the frame field corresponding to the raster segmentation.
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