  Building extraction in VHR RSIs remains a challenging task due to occlusion
and boundary ambiguity problems. Although conventional convolutional neural
networks (CNNs) based methods are capable of exploiting local texture and
context information, they fail to capture the shape patterns of buildings,
which is a necessary constraint in the human recognition. To address this
issue, we propose an adversarial shape learning network (ASLNet) to model the
building shape patterns that improve the accuracy of building segmentation. In
the proposed ASLNet, we introduce the adversarial learning strategy to
explicitly model the shape constraints, as well as a CNN shape regularizer to
strengthen the embedding of shape features. To assess the geometric accuracy of
building segmentation results, we introduced several object-based quality
assessment metrics. Experiments on two open benchmark datasets show that the
proposed ASLNet improves both the pixel-based accuracy and the object-based
quality measurements by a large margin. The code is available at:
https://github.com/ggsDing/ASLNet
