Change-Aware Network for Damaged Roads Recognition and Assessment Based on Multi-temporal Remote Sensing Imageries

Road damage assessment holds tremendous potential in evaluating damages and reducing disaster risks to human lives during emergency responses to unforeseen events. The Change Detection (CD) method detects changes in the land surface by comparing bi-temporal remote sensing imageries. Using the CD method for post-disaster assessment, existing research mainly focuses on building, while in terms of road, both the dataset and methodology need to be improved. In response to this, we propose an innovative multi-tasking network that combines Vision Transformer and UNet (BiTransUNet) for identifying road change areas and damage assessments from bi-temporal remote sensing imageries before and after natural disasters, moreover, propose the first road damage assessment model. Notably, our BiTransUNet comprises three efficient modules: Multi-scale Feature Extraction (MFE) module for extracting multi-scale features, Trans and Res Skip Connection (TRSC) module for modeling spatial-temporal global information, and Dense Cased Upsample (DCU) module for change maps reconstruction. In addition, to facilitate our study, we create a new Remote Sensing Road Damage Dataset, RSRDD, thoughtfully designed to contain 1,212 paired imageries before and after disasters, and the corresponding road change masks and road damage levels. Our experimental results on the proposed RSRDD show that our BiTransUNet outperforms current state-of-the-art approaches. BiTransUNet is also applied on the LEVIR-CD building change detection dataset and achieved the best performance, which demonstrates its compatibility in detecting changes of different important ground objects.