Polygonal Building Extraction by Frame Field Learning

While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To help bridge the gap between deep network output and the format used in downstream tasks, we add a frame field output to a deep segmentation model for extracting buildings from remote sensing images. We train a deep neural network that aligns a predicted frame field to ground truth contours. This additional objective improves segmentation quality by leveraging multi-task learning and provides structural information that later facilitates polygonization; we also introduce a polygonization algorithm that that utilizes the frame field along with the raster segmentation.

Automated summary

The research team enhanced a deep segmentation model by adding frame field output to facilitate the conversion of raster segmentations to vector polygons required by geographic information systems. Training a neural network to align predicted frame fields with ground truth contours improved segmentation quality and enabled polygonization.