BDD-Net: A General Protocol for Mapping Buildings Damaged by a Wide Range of Disasters Based on Satellite Imagery

Abstract

The timely and accurate recognition of damage to buildings after destructive disasters is one of the most important post-event responses. Due to the complex and dangerous situations in affected areas, field surveys of post-disaster conditions are not always feasible. The use of satellite imagery for disaster assessment can overcome this problem. However, the textural and contextual features of post-event satellite images vary with disaster types, which makes it difficult to use models that have been developed for a specific disaster type to detect damaged buildings following other types of disasters. Therefore, it is hard to use a single model to effectively and automatically recognize post-disaster building damage for a broad range of disaster types. Therefore, in this paper, we introduce a building damage detection network (BDD-Net) composed of a novel end-to-end remote sensing pixel-classification deep convolutional neural network. BDD-Net was developed to automatically classify every pixel of a post-disaster image into one of non-damaged building, damaged building, or background classes. Pre- and post-disaster images were provided as input for the network to increase semantic information, and a hybrid loss function that combines dice loss and focal loss was used to optimize the network. Publicly available data were utilized to train and test the model, which makes the presented method readily repeatable and comparable. The protocol was tested on images for five disaster types, namely flood, earthquake, volcanic eruption, hurricane, and wildfire. The results show that the proposed method is consistently effective for recognizing buildings damaged by different disasters and in different areas.

MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection

“…Specifically, a mathematical formula for the effective number of samples in [37] was adopted, which preferred to assign higher weights to changed pixels' loss. Focal loss was combined to form the final effective sample number adaptively weighted loss (EAWLoss), which confirmed the effectiveness of the pixel-level binary classification task and can be extended to a multiclass task such as semantic change detection [38][39][40].…”

“…Specifically, a mathematical formula for the effective number of samples in [37] was adopted, which preferred to assign higher weights to changed pixels' loss. Focal loss was combined to form the final effective sample number adaptively weighted loss (EAWLoss), which confirmed the effectiveness of the pixel-level binary classification task and can be extended to a multiclass task such as semantic change detection [38][39][40]. Our contributions can be summarized as follows: (1) We proposed a novel end-to-end framework called a multi-level change contextual refinement net (MCCRNet) for the change detection of bi-temporal remote sensing images.…”

Structural engineering from an inverse problems perspective

“…Other researchers adopted satellite images to examine damage status over a larger scope of work (i.e. multiple buildings at the community level) after natural disasters such as flood, earthquake, volcanic eruption, hurricane and wildfire [222,223].…”

A Two-Stage Seismic Damage Assessment Method for Small, Dense, and Imbalanced Buildings in Remote Sensing Images

“…Many researchers [40][41][42][43] began to use the xBD dataset as benchmark dataset for automated building damage assessment studies. Shao et al [41] reported a new endto-end remote sensing pixel-classification CNN to classify each pixel of a post-disaster image as an undamaged building, damaged building, or background.…”

“…Many researchers [40][41][42][43] began to use the xBD dataset as benchmark dataset for automated building damage assessment studies. Shao et al [41] reported a new endto-end remote sensing pixel-classification CNN to classify each pixel of a post-disaster image as an undamaged building, damaged building, or background. During the training, both pre-and post-disaster images were used as inputs to increase semantic information, while the dice loss and focal loss functions were combined to optimize the model for solving the imbalance problem.…”