Geometry‐guided semantic segmentation for post‐earthquake buildings using optical remote sensing images

Wang, Yu; Xin, Jing; Xu, Yang; Cui, Liangyi; Zhang, Qiangqiang; Li, Hui

doi:10.1002/eqe.3966

Cited by 10 publications

(2 citation statements)

References 75 publications

(142 reference statements)

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“…For instance, Wang et al designed a novel loss function based on U-Net [53], incorporating both geometric consistency constraint and cross-entropy losses. This loss function is employed for seismic post-disaster building segmentation across multiple scales with complex geometry.…”

Section: Transfer Learningmentioning

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

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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.

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Section: Transfer Learningmentioning

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

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“…A novel loss function named geometric consistency enhanced (GCE) loss is designed considering the geometrical constraints of split line length, curvature, and area to focus on local boundaries and improve the segmentation details [15][16][17]:…”

Section: Boundary Refinement By Geometric Consistencymentioning

confidence: 99%

Coarse-to-Fine Seismic Assessment Based on Computer Vision

XU,

ZHANG,

2023

Proceedings of the 14th International Workshop on Structural Health Monitoring

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Recently, remote sensing satellites, unmanned aerial vehicles (UAVs), and smartphones have been extensively utilized in non-contact post-earthquake inspection at different scales with cutting-edge computer vision and machine learning techniques. This study establishes a computer-vision-based coarse-to-fine seismic assessment framework to localize dense buildings in urban areas, classify collapsed and noncollapsed states, recognize multi-type surface damage on structural components, and evaluate seismic performances. First, a Transformer-CNN deep learning architecture is designed for semantic segmentation of dense buildings and binary classification of collapsed states using large-scale remote sensing satellite images. It consists of a Swin Transformer encoder, multi-stage feature fusion module, and UPerNet decoder to extract global correlations and local features of dense buildings synchronously. Then, a multi-task learning strategy is proposed to simultaneously recognize multi-type structural components (column, beam, wall), seismic damage (concrete crack, spalling, and rebar exposure), and multi-level damage states (minor, moderate, major) using medium-scale UAV images. It contains a CNN-based encoder-decoder backbone with skip-connection modules and multi-head segmentation subnetworks for different tasks. The geometric consistency loss of split line, area, and curvature is further designed to refine the semantic segmentation of local details, increase boundary smoothness, and suppress inner voids. Finally, a machine learning neural network is established to quantify the seismic damage index of structural components using damage-related parameters (lengths, areas, and numbers of concrete crack, spalling, and rebar exposure) and design-related parameters (axial compression ratio, shear span ratio, and volumetric stirrup ratio) as inputs. A seismic damage indicator with an explicit bound of [0,1] can be obtained to reflect the nonlinear accumulation of seismic damage. The effectiveness and applicability under real-world post-earthquake scenarios have been validated by the 2017 Mexico City Earthquake M7.1, 2008 Beichuan Earthquake M8.0, 2010 Yushu Earthquake M6.9, 2015 Nepal Earthquake M7.8, 2016 Ecuador Earthquake M7.8, and 2016 Meinong Earthquake M6.7.

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