TDA-Net: A Novel Transfer Deep Attention Network for Rapid Response to Building Damage Discovery

Abstract

The rapid and accurate discovery of damage information of the affected buildings is of great significance for postdisaster emergency rescue. In some related studies, the models involved can detect damaged buildings relatively accurately, but their time cost is high. Models that can guarantee both detection accuracy and high efficiency are urgently needed. In this paper, we propose a new transfer-learning deep attention network (TDA-Net). It can achieve a balance of accuracy and efficiency. The benchmarking network for TDA-Net uses a pair of deep residual networks and is pretrained on a large-scale dataset of disaster-damaged buildings. The pretrained deep residual networks have strong sensing properties on the damage information, which ensures the effectiveness of the network in prefeature grasping. In order to make the network have a more robust perception of changing features, a set of deep attention bidirectional encoding and decoding modules is connected after the TDA-Net benchmark network. When performing a new task, only a small number of samples are needed to train the network, and the damage information of buildings in the whole area can be extracted. The bidirectional encoding and decoding structure of the network allows two images to be input into the model independently, which can effectively capture the features of a single image, thereby improving the detection accuracy. Our experiments on the xView2 dataset and three datasets of disaster regions achieve high detection accuracy, which demonstrates the feasibility of our method.

Performance Improvement of Deep Convolutional Networks for Aerial Imagery Segmentation of Natural Disaster-Affected Areas

“…Research [39] used a learning rate of 1 × 10 -5 , the maximum number of epochs was 150, with a batch size of 16, the division of the dataset for training was 70%, and validation was 30%, and the Adam optimizer. Research [40] chose Adam as the optimizer, and the learning rate was 1 × 10 -4 . Research [41] used augmentation consisting of random rotations and vertical and horizontal flips; the model was trained for 200 epochs with a dynamic learning rate of 0.001, Adam was used as optimization, the model was trained with four different batch sizes (16,32,64,128), and 30% of each dataset was used as validation data.…”

“…The Red Relief Image Map (RRIM) dataset was used in the study [39]. The xView2 dataset, WHU Building, and other data from Google Earth were used in the study [40]. Research [41] used three different datasets created by RapidEye, the Normalized Vegetation Index (NDVI), and the digital elevation model (DEM).…”

“…Other studies analyzed and evaluated the performance of popular semantic segmentation methods named DeepLabv3+, Pyramid Scene Parsing Network (PSPNet), and ENet on problems related to natural disaster datasets [27], detecting and segmenting important objects in aerial footage of disaster locations used Mask-Region Based Convolutional Neural Networks (Mask-RCNN) and PSPNet [28], segmentation of damage to buildings after a natural disaster used MSNet [29], a self-attention-based semantic segmentation named ReDNet on a disaster UAV dataset and compared with three other advanced segmentation models: ENet, DeepLabv3+, and PSPNet [30], flood detection based on CNN AlexNet to extract flood-related features from disaster zone images [31], semantic segmentation of aerial images for post-flood landscape understanding by applying three advanced semantic segmentation networks namely ENet, PSPNet, and DeepLabv3+ [32], detecting buildings damaged after an earthquake used a network model Convolutional neural network VGG-16, VGG-19, and NASNet [33], semantic segmentation of natural disaster datasets used self-attentionbased methods combined with Global Average Pooling and U-Net [34], semantic segmentation of post-flood datasets with U-Net, PSPNet, and DeepLabV3+ [35], detecting flooding used segmentation with three deep neural networks: PSPNet, DeepLabV3, and U-Net [36], and extracted residential buildings with a modified Mask R-CNN [37], semantic segmentation of volcanic ash eruptions used SegNet and U-Net convolutional neural networks for volcano monitoring in volcanic eruptions [38], landslide detection and identification used Lightweight Attention U-Net [39], finding buildings damaged by disasters used transfers-learning deep attention network (TDA-Net) [40], and semantic segmentation to detect landslides used U-Net [41], [42], and self-training method [43].…”

Data-Driven Earthquake Multi-Impact Modelling: A Comparison of Models

“…Furthermore, the building type data generally have low spatial resolution (mostly administrative level one or zero 14 ) and is often biased toward certain regions of the world 11 . Recent research has also applied deep-learning models to predict building damage by comparing satellite image data before and after floods, earthquakes and conflict, using purely data-driven deep learning models [15][16][17][18] . This requires satellite image data, where the models in these (pre-print only) articles ( [15][16][17][18] ) were trained on commercial (non-publicly available) satellite data.…”

“…Recent research has also applied deep-learning models to predict building damage by comparing satellite image data before and after floods, earthquakes and conflict, using purely data-driven deep learning models [15][16][17][18] . This requires satellite image data, where the models in these (pre-print only) articles ( [15][16][17][18] ) were trained on commercial (non-publicly available) satellite data. This approach limits the outcome predictor to building damage classification, and not mortality or displacement.…”