In the field of post-disaster assessment, for timely and accurate rescue and
localization after a disaster, people need to know the location of damaged
buildings. In deep learning, some scholars have proposed methods to make
automatic and highly accurate building damage assessments by remote sensing
images, which are proved to be more efficient than assessment by domain
experts. However, due to the lack of a large amount of labeled data, these
kinds of tasks can suffer from being able to do an accurate assessment, as the
efficiency of deep learning models relies highly on labeled data. Although
existing semi-supervised and unsupervised studies have made breakthroughs in
this area, none of them has completely solved this problem. Therefore, we
propose adopting a self-supervised comparative learning approach to address the
task without the requirement of labeled data. We constructed a novel asymmetric
twin network architecture and tested its performance on the xBD dataset.
Experiment results of our model show the improvement compared to baseline and
commonly used methods. We also demonstrated the potential of self-supervised
methods for building damage recognition awareness.

本文针对自然灾害后修复领域中建筑物损伤图像处理问题，借助自监督孪生网络，提出了一种无需大量标注数据的方案，并在 xBD 数据集上得到了相对于基础模型和常用方法更好的表现，验证了自监督学习在建筑损伤图像识别方面的潜力。

大规模 xBD 卫星图像基准数据集中用于建筑物损伤评估的自监督学习

Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets

Traditional post-disaster assessment of damage heavily relies on expensive
GIS data, especially remote sensing image data. In recent years, social media
has become a rich source of disaster information that may be useful in
assessing damage at a lower cost. Such information includes text (e.g., tweets)
or images posted by eyewitnesses of a disaster. Most of the existing research
explores the use of text in identifying situational awareness information
useful for disaster response teams. The use of social media images to assess
disaster damage is limited. In this paper, we propose a novel approach, based
on convolutional neural networks and class activation maps, to locate damage in
a disaster image and to quantify the degree of the damage. Our proposed
approach enables the use of social network images for post-disaster damage
assessment and provides an inexpensive and feasible alternative to the more
expensive GIS approach.

本文提出了一种基于卷积神经网络和类别激活映射的新方法，用于在社交媒体图像中定位灾害损失并量化其程度，从而提供一种相对于昂贵的地理信息系统方法更为经济有效的、基于社交网络图像的灾后损失评估方案。