Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images

Hong, Zhonghua; Zhong, Hongzheng; Pan, Haiyan; Li, Jun; Zhou, Ruyan; Zhang, Yun; Han, Yanling; Wang, Jing; Yang, Shuhu; Zhong, Changyue

doi:10.3390/s22155920

Cited by 21 publications

(10 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By using historical earthquake data for model training and fine-tuning the model after a new earthquake, Hong Zhonghua et al were able to classify damaged buildings quickly and accurately, improving the ability to classify buildings with different damage levels [13].…”

Section: Earthquakesmentioning

confidence: 99%

Application of UAV remote sensing in natural disaster monitoring and early warning: an example of flood and mudslide and earthquake disasters

Chen

2024

HSET

View full text Add to dashboard Cite

As the frequency and complexity of natural disasters increase, effective monitoring and early warning have become important for the protection of life and property. This paper discusses the role of unmanned aerial vehicle (UAV) remote sensing technology in natural disaster monitoring and early warning. The research in this paper finds that the advantages of UAVs, which carry multiple sensors and are convenient and dexterous, are conducive to natural disaster monitoring. The article then provides examples of UAV applications in different natural disaster scenarios, including monitoring, early warning, post-disaster assessment and ecological restoration in floods, mudslides and earthquakes. In flood monitoring, UAVs equipped with various sensors, such as multi-spectral sensors and infrared thermal imagers, can quickly scan flood-prone areas and transmit data in real time for timely warning and rescue. In mudslide monitoring, drones can collect data such as surface temperature, soil moisture and vegetation health to help identify signs of potential danger. In earthquake monitoring, drones can provide high-resolution images and video to assess earthquake damage and the condition of infrastructure. The future technological innovation and industry development of UAV remote sensing will continue to progress in terms of sensor technology innovation, application of machine learning and artificial intelligence, range extension, and convergence of communication technologies. The significance of this paper is to highlight the importance of UAV remote sensing in natural disaster management and to provide a vision for future research and applications. Drones will continue to play a key role in facilitating the efficiency and accuracy of natural disaster monitoring and early warning to better address potential threats.

show abstract

Section: Earthquakesmentioning

confidence: 99%

Application of UAV remote sensing in natural disaster monitoring and early warning: an example of flood and mudslide and earthquake disasters

Chen

2024

HSET

View full text Add to dashboard Cite

show abstract

“…In high-resolution remote sensing images, building changes can be monitored at a detailed scale. Deep convolutional networks [ 12 ] (ConvNets) are capable of extracting powerful discriminative features from these images. Currently, cutting-edge change detection methods primarily rely on deep convolutional networks.…”

Section: Introductionmentioning

confidence: 99%

Siamese Transformer-Based Building Change Detection in Remote Sensing Images

Xiong,

Liu,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

To address the challenges of handling imprecise building boundary information and reducing false-positive outcomes during the process of detecting building changes in remote sensing images, this paper proposes a Siamese transformer architecture based on a difference module. This method introduces a layered transformer to provide global context modeling capability and multiscale features to better process building boundary information, and a difference module is used to better obtain the difference features of a building before and after a change. The difference features before and after the change are then fused, and the fused difference features are used to generate a change map, which reduces the false-positive problem to a certain extent. Experiments were conducted on two publicly available building change detection datasets, LEVIR-CD and WHU-CD. The F1 scores for LEVIR-CD and WHU-CD reached 89.58% and 84.51%, respectively. The experimental results demonstrate that when utilized for building change detection in remote sensing images, the proposed method exhibits improved robustness and detection performance. Additionally, this method serves as a valuable technical reference for the identification of building damage in remote sensing images.

show abstract

“…Hong [16] proposed a deep learning-based Multi-View Stereo (MVS) model for reconstructing 3D models of earthquake-damaged buildings, aimed at assisting in building damage assessment tasks. Hong [17] presented EBDC-Net (Earthquake Building Damage Classification Network). The network comprises a feature extraction module and a damage classification module and was designed to augment semantic information and differentiate various damage levels.…”

Section: Introductionmentioning

confidence: 99%

BDHE-Net: A Novel Building Damage Heterogeneity Enhancement Network for Accurate and Efficient Post-Earthquake Assessment Using Aerial and Remote Sensing Data

Liu,

Luo,

Chen

et al. 2024

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Accurate and efficient post-earthquake building damage assessment methods enable key building damage information to be obtained more quickly after an earthquake, providing strong support for rescue and reconstruction efforts. Although many methods have been proposed, most have limited effect on accurately extracting severely damaged and collapsed buildings, and they cannot meet the needs of emergency response and rescue operations. Therefore, in this paper, we develop a novel building damage heterogeneity enhancement network for pixel-level building damage classification of post-earthquake unmanned aerial vehicle (UAV) and remote sensing data. The proposed BDHE-Net includes the following three modules: a data augmentation module (DAM), a building damage attention module (BDAM), and a multilevel feature adaptive fusion module (MFAF), which are used to alleviate the weight deviation of intact and slightly damaged categories during model training, pay attention to the heterogeneous characteristics of damaged buildings, and enhance the extraction of house integrity contour information at different resolutions of the image. In addition, a combined loss function is used to focus more attention on the small number of severely damaged and collapsed classes. The proposed model was tested on remote sensing and UAV images acquired from the Afghanistan and Baoxing earthquakes, and the combined loss function and the role of the three modules were studied. The results show that compared with the state-of-the-art methods, the proposed BDHE-Net achieves the best results, with an F1 score improvement of 6.19–8.22%. By integrating the DBA, BDAM, and MFAF modules and combining the loss functions, the model’s classification accuracy for severely damaged and collapsed categories can be improved.

show abstract

Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images

Cited by 21 publications

References 32 publications

Application of UAV remote sensing in natural disaster monitoring and early warning: an example of flood and mudslide and earthquake disasters

Application of UAV remote sensing in natural disaster monitoring and early warning: an example of flood and mudslide and earthquake disasters

Siamese Transformer-Based Building Change Detection in Remote Sensing Images

BDHE-Net: A Novel Building Damage Heterogeneity Enhancement Network for Accurate and Efficient Post-Earthquake Assessment Using Aerial and Remote Sensing Data

Contact Info

Product

Resources

About