Building Footprint Extraction in Dense Areas using Super Resolution and
  Frame Field Learning
Vuong Nguyen Anh Ho Duc-Anh Vu Nguyen Thi Ngoc Anh Tran Ngoc Thang
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.