Automatic Building Extraction in Aerial Scenes Using Convolutional Networks
This provides a scalable solution for automating labor-intensive building extraction in geographic information systems, though it is incremental in method.
The paper tackles the challenge of automatic building extraction from aerial imagery by designing a convolutional network with a multi-stage integration and a signed distance function representation, achieving superior performance on large, complex datasets.
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.