  Building extraction aims to segment building pixels from remote sensing
images and plays an essential role in many applications, such as city planning
and urban dynamic monitoring. Over the past few years, deep learning methods
with encoder-decoder architectures have achieved remarkable performance due to
their powerful feature representation capability. Nevertheless, due to the
varying scales and styles of buildings, conventional deep learning models
always suffer from uncertain predictions and cannot accurately distinguish the
complete footprints of the building from the complex distribution of ground
objects, leading to a large degree of omission and commission. In this paper,
we realize the importance of uncertain prediction and propose a novel and
straightforward Uncertainty-Aware Network (UANet) to alleviate this problem. To
verify the performance of our proposed UANet, we conduct extensive experiments
on three public building datasets, including the WHU building dataset, the
Massachusetts building dataset, and the Inria aerial image dataset. Results
demonstrate that the proposed UANet outperforms other state-of-the-art
algorithms by a large margin.
