结合PolSAR影像纹理特征分析提取倒塌建筑物

利用震后1景极化SAR影像提取倒塌建筑物是一种快速有效的灾害调查手段。倒塌建筑和倾斜建筑物在PolSAR影像中的散射特征相似,易造成建筑物倒塌率的过度评估。由于倒塌建筑和倾斜建筑的纹理特征有较大差异,将利用这种纹理差异来解决倒塌建筑和倾斜建筑的混分问题。通过实验发现均值、同质性、熵及相关性4种基于灰度共生矩阵(Gray-Level Co-occurrence Matrix,GLCM)的纹理特征能够有效区分倾斜建筑和倒塌建筑,故利用这4种纹理特征提取倒塌建筑中混杂的倾斜建筑,从而降低倒塌建筑的虚警率。以玉树地震为例,提取城区的建筑物震害信息,实验证明该方法能够大幅提高建筑物震害评估精度。     

Incorporation of BIM-based probabilistic non-structural damage assessment into agent-based post-earthquake evacuation simulation

Earthquakes can cause severe damage to structural and non-structural elements of buildings; consequently, they pose high risks to human lives. To mitigate such risks, attention has been paid to enhancing the indoor environment for increased building safety. Yet little effort has been made to assess a building occupants' evacuation behaviors in response to damage to the indoor environment. This paper addresses this issue with a novel simulation framework that couples human behaviors with changes to the indoor building environment during post-earthquake evacuation. In particular, we present a building information modelling (BIM)-based prototype that simulates seismic damage to the non-structural indoor elements and visualizes its impacts on evacuation using a color-coded heat map. The simulated damage is then used as input to an agent-based model for post-earthquake evacuation. Using a probabilistic method to assess the non-structural elements' damage states, we are able to evaluate the impact of indoor damage on the evacuation process. We performed a trial of our prototype for a hypothetical earthquake in an educational building. The results revealed how the average evacuation time would increase as the earthquake intensity increases (from 38.6 s for the no-damage scenario to 122.9 for the highest-damage scenario). The proposed prototype has the potential to be joined with other tools, such as finite-element-based simulation, to incorporate structural analysis as well. Planners and designers can explicitly use our model's output to analyze the post-earthquake evacuation with the indoor non-structural damage to assess different building design geometries that increase the chances of a suitable evacuation process.     

双向地震激励下隔震结构抗倾覆特性的数值分析

为研究叠层橡胶支座隔震建筑的抗倾覆性能,建立隔震结构在双向地震激励下倾覆力矩时域响应动力分析模型.在对该模型进行简化的基础上,利用结构设计反应谱探讨结构高宽比和结构基本周期等因素对隔震结构抗倾覆力矩与倾覆力矩比值的影响.给出多层和小高层隔震结构在双向地震作用下的抗倾覆安全因数随地震烈度、场地土类别的变化规律.利用本课题...     

A Summary of Research Methods for Seismic Damage Information Recognition of Polarized SAR Buildings

Synthetic Aperture Radar (SAR) can observe the Earth without the influence of the weather and sunlight, and Polarimetric SAR (PolSAR) even could acquire four kinds of polarization information at the same time. Therefore, extracting post-earthquake damage information by use of PolSAR has the advantage of timeliness and accuracy. This paper shows a summary of the methods for extracting seismic damage information based on PolSAR data. It firstly review the development of PolSAR and then summarizes the application and comparative analysis of the data types (multi-source data, multi-temporal data and single-temporal data) for extracting seismic damage of buildings in the past 10 years. Next, the methods of building earthquake damage extraction based on polarization decomposition and polarization characteristics and texture features is summarized. Finally, the research work is proposed to supplement the deficiency of PolSAR in earthquake damage extraction accuracy with the combination of geographic information data POI.     

极化SAR建筑物震害信息识别研究方法综述

合成孔径雷达具有全天候、全天时的对地观测优势,全极化合成孔径雷达（PolSAR）能够同时获取4种极化信息,利用PolSAR在震后进行震害评估具有及时性和准确性的优势。首先概述了PolSAR的发展状况及其在建筑物震害信息提取中的应用;其次,基于不同数据类型（多时相数据、多源数据、单时相数据）,概述了近10 a来 PolSAR数据在建筑物震害提取中的应用及其对比分析;然后,从极化分解方法、极化特征以及纹理特征3个方面对PolSAR数据的建筑物震害提取方法进行了详细阐述;最后,提出未来研究工作的设想,以期结合地理信息数据POI补充PolSAR在震害评估精度方面的不足。     

Integration of super-pixel segmentation and deep-learning methods for evaluating earthquake-damaged buildings using single-phase remote sensing imagery

ABSTRACT

Rapid identification of post-earthquake collapsed buildings can be used to conduct immediate damage assessments (scope and extent), which could potentially be conducive to the formulation of emergency response strategies. Up to the present, the assessments of earthquake damage are mainly achieved through artificial field investigations, which are time-consuming and cannot meet the urgent requirements of quick-response emergency relief allocation. In this research study, an intelligent assessment method based on deep-learning, super-pixel segmentation, and mathematical morphology was proposed to evaluate the damage degrees of earthquake-damaged buildings. This method firstly utilized the Deeplab v2 neural network to obtain the initial damaged building areas. Then, the simple linear iterative cluster (SLIC) method was employed to segment the test images so as to accurately extract the area boundaries of the earthquake-damaged buildings. Next, the images subdivided by SLIC can be merged according to the initial damaged building areas identified by Deeplab v2 neural network. Finally, a mathematical morphological method was introduced to eliminate the background noise. Experimental results demonstrated that the proposed algorithm was superior to others in both convergent speed and accuracy. Besides, its parameter selection was flexible and easily realized which was of great significance to earthquake damage assessments and provided valuable guidance for the formulation of future emergency response plans after earthquake events.     

基于多纹理特征融合的震后SAR图像倒塌建筑物信息提取

合成孔径雷达（SAR）凭借其全天候观测能力以及SAR图像中丰富的纹理信息,在震后建筑物倒塌评估中发挥了重要作用。针对SAR图像中倒塌建筑物纹理特征多样但利用率较低,且特征信息冗余的问题,提出一种基于主成分分析的SAR图像多纹理特征分类方法。该方法基于灰度直方图、灰度共生矩阵、局部二值模式、Gabor滤波器提取了26种纹理特征信息,构建主成分变量进行多维特征优选与降维融合,通过随机森林分类算法提取建筑物的倒塌信息。以2016年日本熊本地震为例验证了该方法的有效性,结果显示其提取精度高达79.85%,倒塌建筑物的识别效率有所提高,分类结果优于单种纹理特征提取方法及多种纹理特征组合提取法,可用于震后建筑物震害信息的快速提取。     

Extraction of earthquake-induced collapsed buildings using very high-resolution imagery and airborne lidar data

Extraction of urban building damage caused by earthquake disasters, from very-high-resolution (VHR) satellite imagery and related geospatial data, has been widely studied in the past decade. In this study, a multi-stage collapsed building detection method, using bi-temporal (pre- and post-earthquake) VHR images and post-earthquake airborne light detection and ranging (lidar) data, is proposed. Ground objects that are intact and significantly different from collapsed buildings, such as intact buildings, pavements, shadows, and vegetation, were first extracted using the post-event VHR image and lidar data and masked out. Collapsed buildings were then extracted by classifying the combined bi-temporal VHR images and texture images of the remaining area using a one-class classifier, the One-Class Support Vector Machine (OCSVM). A post-processing procedure was adopted to refine the obtained result. The proposed method was quantitatively evaluated and compared to two existing methods in Port au Prince, Haiti, which was heavily hit by an earthquake in January 2010. In the two comparative methods, data for the whole study area were directly used. In the first method, collapsed buildings were extracted directly using the OCSVM, while in the second method, buildings and pavements were removed from the extraction result of the first method. The results showed that the proposed method significantly outperformed the existing methods, with increases of 21% and 40%, respectively, in the kappa coefficient. The proposed method provides a fast and reliable method to detect collapsed urban buildings caused by earthquake disasters, and could also be applied to other study areas using similar data combinations.     

Building‐based damage detection due to earthquake using the watershed segmentation of the post‐event aerial images

This paper presents an approach for detecting the damaged buildings due to earthquake using the watershed segmentation of the post‐event aerial images. The approach utilizes the relationship between the buildings and their cast shadows. It is based on an idea that if a building is damaged, it will not produce shadows. The cast shadows of the buildings are detected through an immersion‐based watershed segmentation. The boundaries of the buildings are available and stored in a GIS as vector polygons. The vector‐building boundaries are used to match the shadow casting edges of the buildings with their corresponding shadows and to perform assessments on a building‐specific manner. For each building, a final decision on the damage condition is taken, based on the assessments carried out for that building only. The approach was implemented in Golcuk, one of the urban areas most strongly hit by the 1999 Izmit, Turkey earthquake. To implement the approach, a system called the Building‐Based Earthquake Damage Assessment System was developed in MATLAB. Of the 284 buildings processed and analysed, 229 were correctly labelled as damaged and undamaged, providing an overall accuracy of 80.63%.     

雷达遥感建筑物震害信息提取方法综述

合成孔径雷达遥感具有全天时、全天候工作的能力,在地震灾害应急中发挥了重要作用。回顾了雷达遥感建筑物震害信息提取技术的发展历史,总结了各种用于建筑物震害信息提取的雷达卫星的参数特征以及使用雷达遥感提取建筑物震害信息的震例。介绍了目前应用的各种雷达遥感建筑物震害信息提取方法,包括目视解译和计算机自动信息提取两种,其中后者包括基于纹理特征和极化特征的单时相方法、基于强度特征和相干特征的多时相方法。比较了这些方法的适用性和不足,并探讨了雷达遥感建筑物震害信息提取方法的发展趋势。     

Numerical calibration of an easy method for seismic behaviour assessment on large scale of masonry building aggregates

The paper deals with the numerical calibration of a speedy procedure for large scale seismic vulnerability assessment of masonry building aggregates, which are typical building compounds diffused within historical centres of many Italian towns. First of all, based on several numerical analyses developed with the 3MURI calculation program, this simplified assessment procedure has been implemented, it being derived from the well known vulnerability form for masonry buildings integrated by five parameters accounting for the aggregate conditions among adjacent units. Later on, the set-up procedure has been validated through an application to a single building aggregate in the Vesuvius area. Since the results previously achieved have been again confirmed, subsequently the procedure has been used to investigate a wide area of the historical centre of Torre del Greco, allowing for the knowledge of the buildings most at risk under earthquake.Finally, the methodology has been applied to the historical centre of Poggio Picenze (AQ), damaged by the recent Italian earthquake (2009), in order to prove its effectiveness to foresee the damage level experienced by other types of masonry aggregates under seismic actions.     

基于深度学习的村镇砖(混)木房屋识别

破坏性地震发生后, 相较按照抗震设防标准建设的城市公共设施和居民住宅, 广大无抗震设防的村镇居民自建房屋, 更易发生倒塌甚至完全损毁. 以往地震灾情预评估、地震灾害风险调查、地震重点危险区调研, 依靠专家现场踏勘, 确定不同结构类型建筑物数量及所占比例. 本研究借助深度学习和倾斜摄影技术, 进行砖(混)木结构房屋识别, 郯庐断裂带山东境内砖(混)木房屋影像制作数据集, 训练得到Faster R-CNN模型, 该区域内砖(混)木房屋识别平均精度为91.868%. 结果表明, 本文方法能够对砖(混)木房屋进行有效检测, 可应用于地震行业开展震前、震后各类现场工作, 提高政府部门应急管理能力.     

Structural damage detection using bi-temporal optical satellite images

Multi-temporal satellite imagery is now available at sub-metre accuracy and has been found to be very useful for performing rapid damage assessment on human settlement areas affected by large-scale disasters. In this article, a method of formulating structural damage detection measures based on pre- and post-disaster satellite images is proposed. To validate the proposed damage measures, building-based structural damage assessment is conducted. First, their effectiveness in representing multilevel structural damage is demonstrated using synthetic patterns of building damage. Second, the damage classification accuracy is evaluated by means of a pattern classification approach applied to a pair of bi-temporal satellite images, wherein earthquake damage to hundreds of buildings is assessed. The article concludes that the proposed damage detection measures, which are conceptually simple and computationally efficient, outperform traditional measures, such as linear correlation coefficients.     

基于改进MRF的遥感影像建筑物精提取

随着遥感图像的快速发展与广泛应用,基于遥感影像的建筑物提取能够及时、准确地提取建筑物信息,在地图快速更新、城市管理等应用中具有重要的研究意义。目前经神经网络进行特征分析提取的建筑物灰度图存在图像模糊、错分建筑物等情况,并且需要经过二值化处理才能为后续工作所利用。为了提高分类精度,本文在神经网络初提取的基础上,首先采取大津法分割,形态学处理灰度图。并改进马尔可夫随机场方法,提出根据图像局部邻域特征动态估计先验参数β的新方法,且将原始图像特征引入马尔可夫随机场,对大津法分割的结果进行进一步的分割,并对建筑物边缘的锯齿边界进行修正,以提高分类精度。实验表明,所用方法能够有效减少神经网络提取出的灰度图中的错分建筑物。     

基于对象的最优尺度建筑物信息提取方法

针对基于像素分析方法不适用于高分辨率影像信息提取的问题,提出一种基于对象的图像分析方法来进行城市建筑信息提取。采用多分辨率图像分割方法得到图像对象,提出非监督的最优尺度判定方法解决单尺度分割造成的欠分割和过分割问题。在对象分类提取过程中,结合LiDAR数据的地形表面高程信息和光谱信息对建筑物进行提取,并利用尺寸、空间位置等信息进行误分类修正。实验区域共提取出18个建筑目标,结果表明所提出的方法有效可行。     

On the powerful use of simulations in the Quake-Catcher Network to efficiently position low-cost earthquake sensors

The Quake-Catcher Network (QCN) represents a paradigm shift in seismic networks by involving the general public in the collection, detection, and recognition of seismic events. The QCN uses low-cost sensors connected to volunteer computers across the world to monitor seismic events. The location and density of these sensors can impact the accuracy of event detection. Testing different arrangements of new sensors could disrupt the currently active project; thus such an experiment is best accomplished in a simulated environment.This paper presents an accurate and efficient framework for simulating low-cost QCN sensors and identifying their most effective locations and densities. To build the framework, we extend EmBOINC, an emulator of Berkeley Open Infrastructure for Network Computing (BOINC) projects, to handle the trickle messages generated by sensors connected to volunteer hosts and sent to the QCN server when strong ground motion is detected. EmBOINC allows us to rigorously study QCN simulations at 100,000 or even 1,000,000 sensors, highlight strengths and weaknesses of different sensor density and placement, and test the network with various parameters, conditions, and earthquake scenarios. Results obtained with EmBOINC and presented in this paper show how our simulations can reliably study diverse sensor densities and seismic scenarios under different geographical and infrastructural constraints.     

Damage assessment in urban areas using post-earthquake airborne PolSAR imagery

Synthetic aperture radar (SAR) has often been used in earthquake damage assessment due to its extreme versatility and almost all-weather, day-and-night capability. In this article, we demonstrate the potential to use only post-event, high-resolution airborne polarimetric SAR (PolSAR) imagery to estimate the damage level at the block scale. Intact buildings with large orientation angles have a similar scattering mechanism to collapsed buildings; they are all volume-scattering dominant and reflection asymmetric, which seriously hampers the process of damage assessment. In this article, we propose a new damage assessment method combining polarimetric and spatial texture information to eliminate this deficiency. In the proposed method, the normalized circular-pol correlation coefficient is used first to identify intact buildings aligned parallel with the flight direction of the radar. The ‘homogeneity’ feature of the grey-level co-occurrence matrix (GLCM) is then introduced to distinguish building patches with large orientation angles from the severely damaged class. Furthermore, a new damage assessment index is also introduced to handle the assessment at the level of the block scale. To demonstrate the effectiveness of the proposed approach, the high-resolution airborne PolSAR imagery acquired after the earthquake that hit Yushu County, Qinghai Province of China, is investigated. By comparison with the damage validation map, the results confirm the validity of the proposed method and the advantage of further improving the assessment accuracy without external ancillary optical or SAR data.     

Detecting damaged building parts in earthquake-damaged areas using differential seeded region growing

Acquiring information about earthquake-damaged buildings is essential for effective rescue and restoration operations. Building damage must be assessed to provide detailed information regarding the location and proportion of damage to individual buildings. Automatic processing of damage assessment is also critical in hastening relief efforts. Therefore, we propose a new method for automatically extracting damaged building parts and quantitatively assessing the damage to individual buildings caused by earthquakes. The proposed method consists of four parts: generating differential information, differential seeded region growing (DSRG), rule-based earthquake damage analysis, and accuracy assessment. First, differential information is automatically derived to extract the damage candidates. The damage candidates are then used as seed points for the region growing process to extract damaged building parts without requiring intervention by a human analyst. Then, designed automated extraction rules based on the condition of the collapsed or crushed buildings are used on the DSRG results. We applied the proposed method to both a residential area and a business area in Port-au-Prince, Haiti, and evaluated its accuracy using a visual comparison, a location-based assessment, and a proportion-based assessment. The results of the visual comparison were similar to the reference data, exhibiting location accuracies of 86% and 89% for the chosen residential and business areas, respectively. An assessment of the damage proportion to individual buildings was performed, which showed that the proposed method achieved accuracies of 81% and 84% for the residential and business areas, respectively, and was highly correlated with the reference data. The proposed method can accurately estimate damaged building parts, which can accelerate rapid relief actions in earthquake-damaged areas. In addition, the proposed method promotes cost-effective relief actions because it filters out many intact buildings without omitting damaged buildings.     

Synoptic mapping of high-rise buildings in urban areas based on combined shadow analysis and scale space processing

A highly automated methodology is described to map locations and heights of high-rise buildings from single high-resolution multi-spectral satellite imagery. The approach involves preliminary shadow detection using the Tsai colour invariant transform and scale space processing to identify candidate building pixels. Application of shadow-building and shadow length constraints led to mapping of the location and height of building candidate objects. The approach has been applied to a winter SPOT 5 scene of Beijing, China. Tests of buildings in a suburban area indicate that a high detection rate (93%) can be achieved for buildings taller than 28 m. A height estimation accuracy of 20 m has also been met for these buildings.     

Visualizing the history of living spaces

The technology available to building designers now makes it possible to monitor buildings on a very large scale. Video cameras and motion sensors are commonplace in practically every office space, and are slowly making their way into living spaces. The application of such technologies, in particular video cameras, while improving security, also violates privacy. On the other hand, motion sensors, while being privacy-conscious, typically do not provide enough information for a human operator to maintain the same degree of awareness about the space that can be achieved by using video cameras. We propose a novel approach in which we use a large number of simple motion sensors and a small set of video cameras to monitor a large office space. In our system we deployed 215 motion sensors and six video cameras to monitor the 3,000-square-meter office space occupied by 80 people for a period of about one year. The main problem in operating such systems is finding a way to present this highly multidimensional data, which includes both spatial and temporal components, to a human operator to allow browsing and searching recorded data in an efficient and intuitive way. In this paper we present our experiences and the solutions that we have developed in the course of our work on the system. We consider this work to be the first step in helping designers and managers of building systems gain access to information about occupants' behavior in the context of an entire building in a way that is only minimally intrusive to the occupants' privacy.