During natural disasters, aircraft and satellites are used to survey the impacted regions. Usually human experts are needed to manually label the degrees of the building damage so that proper humanitarian assistance and disaster response (HADR) can be achieved, which is labor-intensive and time-consuming. Expecting human labeling of major disasters over a wide area gravely slows down the HADR efforts. It is thus of crucial interest to take advantage of the cutting-edge Artificial Intelligence and Machine Learning techniques to speed up the natural infrastructure damage assessment process to achieve effective HADR. Accordingly, the paper demonstrates a systematic effort to achieve efficient building damage classification. First, two novel generative adversarial nets (GANs) are designed to augment data used to train the deep-learning-based classifier. Second, a contrastive learning based method using novel data structures is developed to achieve great performance. Third, by using information fusion, the classifier is effectively trained with very few training data samples for transfer learning. All the classifiers are small enough to be loaded in a smart phone or simple laptop for first responders. Based on the available overhead imagery dataset, results demonstrate data and computational efficiency with 10% of the collected data combined with a GAN reducing the time of computation from roughly half a day to about 1 hour with roughly similar classification performances.
翻译:自然灾害期间,飞机和卫星被用来调查受灾地区。通常需要人类专家手工标出建筑物损坏的程度,以便实现适当的人道主义援助和救灾(HADR),这是劳动密集型和耗时的。期待在广泛地区给重大灾害贴上人类标签,大大减缓了HADR的努力。因此,利用尖端人工智能和机器学习技术来加快自然基础设施损坏评估进程以达到有效的HAPR。因此,该文件表明,为了高效率地进行建筑物损坏分类,作出了系统的努力。首先,设计了两个新型的基因对抗网(GANs),以扩大用于培训深层学习的分类师的数据。第二,开发了一种使用新数据结构进行对比式学习的方法,以取得卓越的绩效。第三,利用信息融合,对分类师进行了有效的培训,培训数据样本极少,以加快自然基础设施损坏评估进程,从而实现有效的HADAR。因此,所有分类员都足够小,可以装在智能手机或简单的膝上进行系统化工作。根据现有的高端图像数据集,结果显示数据与计算效率,从大约1小时的GAN的半年计算结果和大约1小时的GAN的计算结果。