Operational earthquake-induced building damage assessment using CNN-based direct remote sensing change detection on superpixel level

Qing, Yuanzhao; Ming, Dongping; Qi, Wen; Weng, Qihao; Xu, Lu; Chen, Yangyang; Zhang, Yi; Zeng, Beichen

doi:10.1016/j.jag.2022.102899

Cited by 30 publications

(21 citation statements)

References 19 publications

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“…High-resolution remote sensing imagery enables the monitoring of large-scale land changes over time [2]. Consequently, land cover change detection has emerged as a fundamental task within the remote sensing discipline, finding extensive applications in land surveying [3], [4], urban research [5]- [7], ecosystem monitoring [8]- [10], disaster detection [11], [12], and others.…”

Section: Introductionmentioning

confidence: 99%

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Zhang,

Liu,

Niu

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Vector Polygons are valuable survey data, serving as crucial outputs of national geographical censuses and a fundamental data source for detecting changes in geographical conditions. Current remote-sensing image change detection methods rely on comparing images but overlook abundant historical vector results, struggle with model generalization, and lack adequate samples. Consequently, change detection remains a manual process primarily, unable to meet the requirements for automated and efficient monitoring of standardized geographical conditions. Hence, this paper proposes a change detection method for land cover vector polygons based on high-resolution remote sensing images and deep learning. Initially, the enhanced simple linear iterative clustering (SLIC) algorithm is applied to segment dual-temporal images from identical regions. Subsequently, an annotated dataset is generated using a multi-scale extraction, cropping-with-inpainting approach. Next, datasets derived from pre-and post-temporal images are used for training and testing, respectively, and the training set is purified by using twoclassifier cross-validation. Finally, an improved object-oriented convolutional neural network (CNN) model performs finegrained scene classification. The change rules and postprocessing method are then integrated to identify changed vector polygons. To validate the effectiveness and superiority of the proposed method, we conducted experiments on land cover change detection using datasets from two study areas. The results indicate that the proposed method achieves precision and recall rates of 91.89% and 94.44% on dataset-1, respectively. Similarly, in dataset-2, the precision and recall rates reach 87.59% and 91.41%, respectively. These findings demonstrate the method's efficacy in detecting changed vector polygons, reducing manual intervention, and enhancing detection efficiency.

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Section: Introductionmentioning

confidence: 99%

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Zhang,

Liu,

Niu

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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“…The fusion structure of this framework is based on deep transfer learning. Qing, et al [16] designed a change detection-based BDD framework based on CNN and super-pixel. This framework is applied in three steps: (1) building extraction based on extra feature enhancement bands, (2) damage detection based on change detection method with pre-event super-pixel constraint strategy, and (3) and (3) a quantitative assessment of the damage using a damage index.…”

Section: Introductionmentioning

confidence: 99%

BDD-Net+: A Building Damage Detection Framework Based on Modified Coat-Net

Seydi

Hasanlou

Chanussot

et al. 2023

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

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An accurate and fast assessment of damaged buildings following a disaster is critical for planning rescue and reconstruction efforts. The damage assessment by traditional methods is time-consuming and with limited performance. In this paper, we propose an end-to-end deep-learning network named Building Damage Detection Network-plus (BDD-Net+). The BDD-Net+ is based on a combination of convolution layers and transformer blocks. The proposed framework takes advantage of the multiscale residual convolution blocks and self-attention layers. The proposed framework consists of four main steps: (1) data preparation, (2) model training, (3) damage map generation and evaluation, and (4) the use of an explainable artificial intelligence (XAI) framework for understanding and interpretation of the operation model. Experimental results include two representative realworld benchmark datasets (i.e., the Haiti earthquake and the Bata explosion). The obtained results illustrate that BDD-Net+ achieves excellent efficacy in comparison with other state-ofthe-art methods. Furthermore, the visualization of the results by XAI shows that BDD-Net+ provides more interpretable and explainable results for damage detection than other studied methods.

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“…The xBD and other similar datasets enable researchers to develop and benchmark viable methodologies for post-hazard damage assessment. Most previous studies in this area fall into the categories of image segmentation [11,12] or object detection [13]. The insights offered include but are not limited to, the effectiveness of "attention mechanisms" and visual transformers built on those mechanisms [14,15,16,17,18], novel convolutional blocks [19] and even graph neural networks that account for dependencies between structure types and their damage conditions [20].…”

Section: Introductionmentioning

confidence: 99%

Deep Ensemble Learning for Rapid Large-Scale Post-Earthquake Damage Assessment---Application to 2023 Kahramanmaraş Earthquake Sequence

Soleimani-Babakamali,

Askari,

Heravi

et al. 2023

Preprint

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Extensive impacted region and government damage surveys following the 2023 Kahramanmaraş earthquake sequence provided a unique opportunity to design and validate a rapid post-event damage assessment framework with satellite images, addressing the typical challenges (e.g., generalization to unseen areas) encountered in rapid regional assessment efforts. By analyzing images from the significantly impacted city of Antakya, we manually identified visual features and compared those with damage states in the survey. Then, an Artificial Intelligence-based framework was developed to automate segmentation and damage assessment processes for future test areas. The resulting tool, dubbed RAPID-A, is an uncertainty-aware ensemble of various deep segmentation models fed with public data released shortly after such incidents. Furthermore, the extent of the damages reported in the survey allowed us to propose and validate a strategy to identify various intensities of damage using a single model, considering the less severe damage as more uncertain. Test case studies in Gaziantep and Kahramanmaraş demonstrate that RAPID-A is generalizable, accurate, and efficient. The significance of this framework lies in its ability to assist in rapid initial damage evaluations before expert dispatch or to replace it in countries that lack the capacity to launch large-scale regional assessment programs to guide foreign support.

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Operational earthquake-induced building damage assessment using CNN-based direct remote sensing change detection on superpixel level

Cited by 30 publications

References 19 publications

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

BDD-Net+: A Building Damage Detection Framework Based on Modified Coat-Net

Deep Ensemble Learning for Rapid Large-Scale Post-Earthquake Damage Assessment---Application to 2023 Kahramanmaraş Earthquake Sequence

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