  Automatic building extraction from aerial imagery has several applications in
urban planning, disaster management, and change detection. In recent years,
several works have adopted deep convolutional neural networks (CNNs) for
building extraction, since they produce rich features that are invariant
against lighting conditions, shadows, etc. Although several advances have been
made, building extraction from aerial imagery still presents multiple
challenges. Most of the deep learning segmentation methods optimize the
per-pixel loss with respect to the ground truth without knowledge of the
context. This often leads to imperfect outputs that may lead to missing or
unrefined regions. In this work, we propose a novel loss function combining
both adversarial and cross-entropy losses that learn to understand both local
and global contexts for semantic segmentation. The newly proposed loss function
deployed on the DeepLab v3+ network obtains state-of-the-art results on the
Massachusetts buildings dataset. The loss function improves the structure and
refines the edges of buildings without requiring any of the commonly used
post-processing methods, such as Conditional Random Fields. We also perform
ablation studies to understand the impact of the adversarial loss. Finally, the
proposed method achieves a relaxed F1 score of 95.59% on the Massachusetts
buildings dataset compared to the previous best F1 of 94.88%.
