  Automatic building extraction from aerial and satellite imagery is highly
challenging due to extremely large variations of building appearances. To
attack this problem, we design a convolutional network with a final stage that
integrates activations from multiple preceding stages for pixel-wise
prediction, and introduce the signed distance function of building boundaries
as the output representation, which has an enhanced representation power. We
leverage abundant building footprint data available from geographic information
systems (GIS) to compile training data. The trained network achieves superior
performance on datasets that are significantly larger and more complex than
those used in prior work, demonstrating that the proposed method provides a
promising and scalable solution for automating this labor-intensive task.
