This paper presents DAHiTrA, a novel deep-learning model with hierarchical transformers to classify building damages based on satellite images in the aftermath of natural disasters. Satellite imagery provides real-time and high-coverage information and offers opportunities to inform large-scale post-disaster building damage assessment, which is critical for rapid emergency response. In this work, a novel transformer-based network is proposed for assessing building damage. This network leverages hierarchical spatial features of multiple resolutions and captures temporal difference in the feature domain after applying a transformer encoder on the spatial features. The proposed network achieves state-of-the-art-performance when tested on a large-scale disaster damage dataset (xBD) for building localization and damage classification, as well as on LEVIR-CD dataset for change detection tasks. In addition, this work introduces a new high-resolution satellite imagery dataset, Ida-BD (related to the 2021 Hurricane Ida in Louisiana in 2021) for domain adaptation. Further, it demonstrates an approach of using this dataset by adapting the model with limited fine-tuning and hence applying the model to newly damaged areas with scarce data.
翻译:本文介绍DAHITrA,这是一个具有等级变压器的新型深层学习模型,在自然灾害发生后根据卫星图像对建筑物损坏进行分类;卫星图像提供实时和高覆盖信息,并提供机会为大规模灾后建筑物损坏评估提供信息,这对于快速应急反应至关重要;在这项工作中,提议建立一个新型变压器网络,用于评估建筑物损坏情况;这一网络利用多个分辨率的等级空间特征,并在应用变压器对空间特征进行编码后,捕捉特征领域的时间差异;拟议的网络在进行大规模灾害损坏数据集(XBD)的测试时,取得最新技术性能,用于构建局部和损坏分类,以及用于改变探测任务的LEVIR-CD数据集;此外,这项工作还引入了新的高分辨率卫星图像数据集,Ida-BD(与2021年路易斯安那州2021年的Ida飓风有关),用于区域适应。此外,它展示了使用这一数据集的方法,即对模型进行有限的微调,从而将模型应用于新损坏的地区。