Deep artificial intelligence applications for natural disaster management systems: A methodological review

Abstract

Deep learning techniques through semantic segmentation networks have been widely used for natural disaster analysis and response. The underlying base of these implementations relies on convolutional neural networks (CNNs) that can accurately and precisely identify and locate the respective areas of interest within satellite imagery or other forms of remote sensing data, thereby assisting in disaster evaluation, rescue planning, and restoration endeavours. Most CNN-based deep-learning models encounter challenges related to the loss of spatial information and insufficient feature representation. This issue can be attributed to their suboptimal design of the layers that capture multiscale-context information and their failure to include optimal semantic information during the pooling procedures. In the early layers of CNNs, the network encodes elementary semantic representations, such as edges and corners, whereas, as the network progresses toward the later layers, it encodes more intricate semantic characteristics, such as complicated geometric shapes. In theory, it is advantageous for a segmentation network to extract features from several levels of semantic representation. This is because segmentation networks generally yield improved results when both simple and intricate feature maps are employed together. This study comprehensively reviews current developments in deep learning methodologies employed to segment remote sensing images associated with natural disasters. Several popular deep learning models, such as SegNet U-Net, FCNs, FCDenseNet, PSPNet, HRNet, and DeepLab, have exhibited notable achievements in various applications, including forest fire delineation, flood mapping, and earthquake damage assessment. These models demonstrate a high level of efficacy in distinguishing between different land cover types, detecting infrastructure that has been compromised or damaged, and identifying regions that are fire-susceptible to further dangers. © 2024 The Author(s)