Damaged Building Extraction Using Modified Mask R-CNN Model Using Post-Event Aerial Images of the 2016 Kumamoto Earthquake

Zhan, Yihao; Liu, Wen; Maruyama, Yoshihiro

doi:10.3390/rs14041002

Cited by 21 publications

(12 citation statements)

References 42 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, Zhan et al modified a deep learning model called Mask R-CNN to extract residential buildings and estimate their damage levels from post-disaster aerial images [42]. The model employed an improved Feature Pyramid Network and online complex example mining.…”

Section: Cnn-based Methodsmentioning

confidence: 99%

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

Gu,

Xie,

Zhang

et al. 2024

Buildings

View full text Add to dashboard Cite

After a disaster, ascertaining the operational state of extensive infrastructures and building clusters on a regional scale is critical for rapid decision-making and initial response. In this context, the use of remote sensing imagery has been acknowledged as a valuable adjunct to simulation model-based prediction methods. However, a key question arises: how to link these images to dependable assessment results, given their inherent limitations in incompleteness, suboptimal quality, and low resolution? This article comprehensively reviews the methods for post-disaster building damage recognition through remote sensing, with particular emphasis on a thorough discussion of the challenges encountered in building damage detection and the various approaches attempted based on the resultant findings. We delineate the process of the literature review, the research workflow, and the critical areas in the present study. The analysis result highlights the merits of image-based recognition methods, such as low cost, high efficiency, and extensive coverage. As a result, the evolution of building damage recognition methods using post-disaster remote sensing images is categorized into three critical stages: the visual inspection stage, the pure algorithm stage, and the data-driven algorithm stage. Crucial advances in algorithms pertinent to the present research topic are comprehensively reviewed, with details on their motivation, key innovation, and quantified effectiveness as assessed through test data. Finally, a case study is performed, involving seven state-of-the-art AI models, which are applied to sample sets of remote sensing images obtained from the 2024 Noto Peninsula earthquake in Japan and the 2023 Turkey earthquake. To facilitate a cohesive and thorough grasp of these algorithms in their implementation and practical application, we have deliberated on the analytical outcomes and accentuated the characteristics of each method through the practitioner’s lens. Additionally, we propose recommendations for improvements to be considered in the advancement of advanced algorithms.

show abstract

Section: Cnn-based Methodsmentioning

confidence: 99%

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

Gu,

Xie,

Zhang

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Li et al [5] introduced Histogram Thresholding Mask R-CNN (HTMask R-CNN), a convolutional neural network based on histogram threshold masking regions, using dynamic grayscale thresholds inferred from the results of dual-object instance segmentation tasks with scarce training data, to extract rural building roofs. Zhan et al [6] developed an instance segmentation model based on Mask R-CNN, feature pyramid networks, and online hard example mining, enhancing the precision of building extraction. Wang et al [7] combined the Path Aggregation Feature Pyramid Network and the Atlas Spatial Pyramid Pool with ResNet-50 as the backbone network for the Mask R-CNN model, training on eight different color-scale sample sets specifically constructed for Beijing's traditional villages.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Remote Sensing Imagery for the Recognition of Traditional Villages

Wang,

Shen

2024

JARD

View full text Add to dashboard Cite

Traditional Chinese villages, vital carriers of traditional culture, have faced significant alterations due to urbanization in recent years, urgently necessitating artificial intelligence data updates. This study integrates high spatial resolution remote sensing imagery with deep learning techniques, proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images. Using 0.54 m spatial resolution imagery of traditional village areas as the data source, this method analyzes the geometric and spectral image characteristics of village building rooftops. It constructs a deep learning feature sample library tailored to the target types. Employing a semantically enhanced version of the improved Mask R-CNN (Mask Region-based Convolutional Neural Network) for building recognition, the study conducts experiments on localized imagery from different regions. The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops, achieving an of 0.7520 and an of 0.7400. It improves the current problem of misidentification and missed detection caused by feature heterogeneity. This method offers a viable and effective approach for industrialized data monitoring of traditional villages, contributing to their sustainable development.

show abstract

“…Xiao et al [15] proposed a dynamic cross-fusion network to enable each task to share features from different CNN layers adaptively and achieved state-of-the-art performance. Zhan et al [16] used the Mask R-CNN to extract information on damaged buildings from post-earthquake remote sensing images and identify the damage level. An improved feature pyramid network was designed, and a detection accuracy of 92% was achieved for the most severely damaged buildings (the overall classification accuracy for four damage classes was 88%).…”

Section: Introductionmentioning

confidence: 99%

Improved Swin Transformer-Based Semantic Segmentation of Postearthquake Dense Buildings in Urban Areas Using Remote Sensing Images

Cui

Jing

Wang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Timely acquiring accurate information on the earthquake-induced damage of buildings is crucial for emergency assessment and post-disaster rescue. Optical remote sensing photography has been a typical method for obtaining seismic data in the early stage after an earthquake due to its wide coverage and fast response speed. Currently, convolutional neural networks (CNNs) are widely applied for remote sensing image recognition. However, insufficient extraction and expression ability of global correlations between local image patches limit the performance of dense building segmentation under real-world scenarios. To address the above issue, this paper proposes an improved Swin Transformer to segment dense urban buildings from remote sensing images with complex backgrounds. The original Swin Transformer is used as a backbone of the encoder, and a convolutional block attention module (CBAM) is employed in the linear embedding and patch merging stages to focus on significant features. Hierarchical feature maps are first fused by convolution operators to strengthen the feature extraction process and adjust the channel of feature dimension, followed by being fed to the UPerNet (as the decoder) to obtain the final segmentation map. Buildings are labeled from the collected remote sensing images of the Yushu and Beichuan earthquakes and classified into collapsed and non-collapsed buildings. Data augmentation transformations of horizontal and vertical flipping, brightness adjustment, uniform fogging, and non-uniform fogging are performed to simulate different photography directions, light overexposure, darkness, and foggy occlusions under actual situations. The effectiveness and superiority of the proposed method over the original Swin Transformer and several mature CNN-based segmentation models are validated by ablation experiments and comparative studies. The results show that the mean intersection over union (mIoU) of the improved Swin Transformer reaches 88.53%, achieving a significant improvement of 1.3% compared to the original Swin Transformer of 87.23%. Furthermore, the improved Swin Transformer can accurately identify dense buildings in remote sensing images under complex weather disturbances. Finally, the results indicate good stability, robustness, and generalization ability of the proposed method in the dense segmentation of seismic buildings.

show abstract

Damaged Building Extraction Using Modified Mask R-CNN Model Using Post-Event Aerial Images of the 2016 Kumamoto Earthquake

Cited by 21 publications

References 42 publications

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

High-Resolution Remote Sensing Imagery for the Recognition of Traditional Villages

Improved Swin Transformer-Based Semantic Segmentation of Postearthquake Dense Buildings in Urban Areas Using Remote Sensing Images

Contact Info

Product

Resources

About