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.

建筑提取是从遥感图像中分割建筑像素的目标，它在城市规划和城市动态监测等多个应用中起着重要作用。本文通过利用深度学习方法中的一种新型不确定性感知网络（UANet），提出了一个解决传统深度学习模型中不确定预测问题的有效解决方案，并通过实验证明了其比其他先进算法具有更好的性能。

通过一种不确定性感知网络进行遥感图像的建筑物提取

Building Extraction from Remote Sensing Images via an Uncertainty-Aware  Network

We propose a method for automatically answering questions about images by
bringing together recent advances from natural language processing and computer
vision. We combine discrete reasoning with uncertain predictions by a
multi-world approach that represents uncertainty about the perceived world in a
bayesian framework. Our approach can handle human questions of high complexity
about realistic scenes and replies with range of answer like counts, object
classes, instances and lists of them. The system is directly trained from
question-answer pairs. We establish a first benchmark for this task that can be
seen as a modern attempt at a visual turing test.

本文提出了一种自动回答关于图像的问题的方法，该方法结合了自然语言处理和计算机视觉的最新进展，采用多世界方法将离散推理与不确定预测结合起来，并在贝叶斯框架下表示对感知世界的不确定性。该方法能够处理有关现实场景的高难度的人类问题，并以计数、物体类别、实例和它们的列表等答案的范围进行回复。该系统直接从问题 - 答案对中进行训练。我们建立了该任务的第一个基准，可视为现代视觉图灵测试的一种尝试。