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An object-based image analysis for building seismic vulnerability assessment using high-resolution remote sensing imagery

机译:基于对象的图像分析,用于使用高分辨率遥感影像进行建筑物地震脆弱性评估

摘要

Building seismic vulnerability assessment plays an important role in formulating pre-disaster mitigation strategies for developing countries. The occurrence of high-resolution satellite sensors has greatly motivated it by providing a promising approach to obtain building information. However, this also brings a big challenge to the accurate building extraction and its coherent integration with the assessment model. The main objective of this paper is to investigate how to extract building attributes from high-resolution remote sensing imagery using the object-based image analysis (OBIA) method, so as to accurately and conveniently assess building seismic vulnerability by the combination of in situ field data. A general framework for the assessment of building seismic vulnerability is presented, including (1) the extraction of building information using OBIA, (2) building height estimation, and (3) the support vector machine (SVM)-based building seismic vulnerability assessment. Particularly, an integrated solution is proposed that merges the strengths of multiple spatial contextual relationships and some typical image object measures, under the unified framework to improve building information extraction at different scale levels as well as for different interest objects. With the aid of 35 building samples from two powerful earthquakes in China, the cloud-free WorldView-2 images and some building structure parameters from field survey were used to quantity the grades of building seismic vulnerability in Wuhan Optics Valley, China. The results show that all 48 buildings among the study area have been well detected with an overall accuracy of 80.67 % and the mean error of heights estimated from building shadow is less than 2 m. This indicates that the integrated analysis strategy based on OBIA is suitable for extracting the building information from high-resolution remote sensing imagery. Additionally, the assessment results using SVM show that the building seismic vulnerability is statistically significantly related to structure types and building heights. Both the proposed OBIA method and its integration strategy with SVM are easily implemented and provide readily interpretable assessment results for building seismic vulnerability. This reveals that the proposed method has a great potential to assist urban planners for making local disaster mitigation planning through the prioritization of intervention measures, such as the reinforcement of walls and the dismantlement of endangered houses.
机译:建筑地震易损性评估在制定发展中国家的灾前减灾战略中发挥着重要作用。高分辨率卫星传感器的出现通过提供有前途的获取建筑物信息的方法极大地激发了它。但是,这也给精确的建筑物提取及其与评估模型的一致集成带来了巨大挑战。本文的主要目的是研究如何使用基于对象的图像分析(OBIA)方法从高分辨率遥感影像中提取建筑物属性,以便通过原位场的组合准确,方便地评估建筑物的地震易损性数据。提出了评估建筑物地震易损性的通用框架,包括(1)使用OBIA提取建筑物信息,(2)建筑物高度估计,以及(3)基于支持向量机(SVM)的建筑物地震易损性评估。尤其是,提出了一种集成解决方案,该解决方案在统一框架下融合了多种空间上下文关系和一些典型图像对象度量的优势,以改善不同比例级别以及针对不同兴趣对象的建筑信息提取。借助来自中国两次强地震的35个建筑样本,使用无云的WorldView-2图像和现场调查中的一些建筑结构参数来对中国武汉光谷的建筑地震脆弱性等级进行定量。结果表明,研究区域内的所有48座建筑物均已被很好地检测到,总准确度为80.67%,并且根据建筑物阴影估算的高度的平均误差小于2 m。这表明基于OBIA的综合分析策略适用于从高分辨率遥感影像中提取建筑物信息。此外,使用支持向量机的评估结果表明,建筑物的地震脆弱性在统计上与结构类型和建筑物高度显着相关。所提出的OBIA方法及其与SVM的集成策略都易于实施,并提供了易于解释的建筑物地震易损性评估结果。这表明,所提出的方法具有很大的潜力,可以通过优先考虑干预措施,例如加固墙壁和拆除濒危房屋,来帮助城市规划者制定本地减灾计划。

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