Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning
This work addresses the challenge of extracting building footprints in dense urban areas, which is crucial for urban planning and mapping, but it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of inaccurate building footprint extraction in dense areas by proposing a framework that uses super resolution and frame field learning, achieving significant performance improvements over state-of-the-art methods on a slum area dataset.
Despite notable results on standard aerial datasets, current state-of-the-arts fail to produce accurate building footprints in dense areas due to challenging properties posed by these areas and limited data availability. In this paper, we propose a framework to address such issues in polygonal building extraction. First, super resolution is employed to enhance the spatial resolution of aerial image, allowing for finer details to be captured. This enhanced imagery serves as input to a multitask learning module, which consists of a segmentation head and a frame field learning head to effectively handle the irregular building structures. Our model is supervised by adaptive loss weighting, enabling extraction of sharp edges and fine-grained polygons which is difficult due to overlapping buildings and low data quality. Extensive experiments on a slum area in India that mimics a dense area demonstrate that our proposed approach significantly outperforms the current state-of-the-art methods by a large margin.