单幅高空间分辨率卫星图像提取建筑物三维信息的方法研究

随着防震减灾事业的发展,对基础地理信息的要求日渐提高,利用卫星遥感影像快速补充与更新已有数据成为获取地理信息的主要方法。文中论述了太阳空间位置、卫星空间位置与建筑物空间位置对平面影像上阴影形状的约束,探讨了垂直于建筑物主方向的阴影平均宽度的统计算法和提取建筑物边界的点生长算法,给出了计算建筑物高度的数学公式,并研究出1种基于单幅高空间分辨率遥感图像数据的建筑物边界及高度快速自动生成技术。以上海市宝山区为试验区,得到了该区建筑物位置、面积和高度信息,经实地验证其试验结果可满足相应种类的灾情评估基础信息的要求     

基于无人机、高分卫星遥感影像的甘肃省陇南市建筑物空间化研究

基于无人机、高分卫星影像资料,通过实地调研与遥感影像对比分析,建立基于无人机、高分卫星遥感影像获取建筑物的技术路线,并以甘肃省陇南市为研究区进行实例验证。研究结果表明:利用无人机航拍进行建筑物识别时,采用倾斜摄影和正射影像相结合的方式,建筑物识别效果较好,尤其是对屋顶相同或类似的不同结构建筑物的识别;基于遥感技术获取建筑物时不仅要建立区域建筑物遥感影像解译标志,还需要借助区域地理环境特征、建筑物排列、占地面积、建筑物阴影等因素进行辅助识别,才能获取较为可靠的结果;陇南市建筑物类型主要有土木(含木构架)结构、砖木结构、砖混结构、框架结构4类,占比分别为19.25%、44.29%、31.32%、5.14%,建筑物遥感解译结果精度在-23.92%~25.28%;基于无人机和卫星遥感影像获取居民地建筑物数据可以用于更新地震应急基础数据库,但存在一定的误差。     

基于多源数据的建筑物空间分布格网化方法研究

利用多源数据将建筑物空间分布格网化,得到格网的单元数据,为区域地震灾害损失预评估和地震灾后快速评估提供较为详尽的建筑物格网数据支撑.以宁夏回族自治区平罗县为例,在数字城市建筑物数据、地理国情监测建筑物数据等基础上,结合高分辨率遥感影像进行目视解译,判读和更新建筑物图斑,再通过外业调绘、无人机航拍等手段对建筑物进行验证调...     

基于遥感影像的建筑物空间分布方法在地震灾害损失预评估中的应用

房屋建筑数据是地震重点危险区预评估工作的基础,需基于获取到的房屋建筑信息开展人员伤亡、经济损失、救援物资需求等预评估工作。历年地震重点危险区预评估工作能够通过现场调查得到的房屋建筑信息占比极小,仅能进行抽样调查。因此,为批量完成危险区内全部房屋建筑损失估算,需基于遥感影像获取房屋建筑矢量数据,并建立数据库。为实现全国地震重点危险区预评估工作中大批量建筑物矢量化数据的快速获取,本文采用基于遥感影像的建筑物空间分布数据批量获取方法,得到地震重点危险区内建筑物空间矢量数据,结合现场抽样调查得到的建筑物属性信息,建立地震重点危险区建筑物空间分布数据库,进而为地震重点危险区灾害损失预评估工作提供数据基础。本文采用的方法可广泛应用于地震重点危险区房屋建筑信息获取工作中,可提高工作效率,降低工作成本,提升预评估工作的科学性和准确性。     

基于多源信息的高空间分辨率人口分布研究

针对已有的人口空间化研究多采用静态数据、时空分辨率较低、在应急救援等方面实用性不高的问题,提出了一种使用高时空分辨率数据,结合城市圈层结构理论和主成分分析法的建筑物尺度人口估算方法。以成都市为例,利用腾讯位置大数据,通过计算不同城市圈层的定位率,得到了成都市不同时段1 km×1 km的人口分布数据。在此基础上,以基于建筑物中心点的泰森多边形为人口分配基本单元,结合宜出行热力数据和POI数据,分别计算其对人口分布的贡献值并赋予计算权值,得到了成都市青羊区建筑物尺度人口分布数据。街道尺度统计数据回归分析的决定系数R~2为0.926 4,总体精度较高,模拟人口分布符合实际情况。     

基于天地图高精度建筑物的四川宝兴县人口估算

为了提高震前灾害风险评估和震后灾情快速评估工作中人口空间分布估计的准确性,利用2016年四川宝兴县乡镇人口数据及天地图中的建筑物数据,运用居住建筑人口密度方法得到四川宝兴县各乡镇居住建筑物尺度的人口分布矢量数据,并利用实地调研获取的单体建筑物实际人口进行精度验证。实验结果表明:以居住建筑物体积作为人口空间分布指示因子建模,得到的拟合精度为0.9027,人口平均相对误差为15.23%,结果具有可靠性,可为震前灾害风险评估和震后灾情快速评估提供更为可靠的数据支撑。     

基于无人机影像的辽宁农居建筑物提取技术与应用研究

本研究以实地调研数据为基准,无人机影像数据分析为验证,进行了基于无人机识别能力范畴的辽宁农居建筑结构类型划分,并根据区域结构类型比例、影像特性等指标优化了农居遥感解译标志整体参数,提取了不同建筑结构的不同解译标志阈值区间,并基于无人机低空正射影像DOM、数字表面模型DSM制定改进型DSM提取算法处理流程与代码,利用Python语言及开发环境完成了遥感影像要素提取与评估系统开发应用。通过最优组合解译标志获取无人机影像解译一致性系数Kappa=0.723,表明制定的解译标志特征对获得具有较好一致性的房屋结构类型具备一定的可行性。以改进型DSM提取算法为核心的建筑物提取软件提取建筑的整体精度为75%,基本满足地震应急需求。     

地震前后遥感影像空间尺度特性与最优空间尺度选择

本文基于不同计算窗口大小的改进局部方差方法, 判定地震后遥感影像上的目标物, 如损毁建筑物、 完好建筑物的最佳空间尺度。 对航片、 QuickBird影像进行了系列实验分析, 得到了在QuickBird影像中城区完好建筑物最佳空间分辨率在2~3 m, 损毁建筑物最佳空间分辨率2~4 m, 航片中城区完好建筑物最佳空间分辨率为3~4 m。 最佳空间分辨率与目标地物的尺度紧密相关, 不同尺度大小的地物具有不同的最优空间尺度。 最佳空间尺度的选择在处理海量高空间分辨率影像时通过重采样选取最佳空间分辨率, 可以有效减少图像运算时间, 在地震灾害快速评估中具有一定的应用意义。     

基于无人机影像的九寨沟地震建筑物震害定量评估

利用2017年8月8日九寨沟7.0级地震震后获取的无人机影像,结合地面震害调查资料,分析各类建筑物震害特征,建立建筑物震害无人机遥感解译标志;选取地震灾区漳扎镇(部分区域)和荷叶寨2个区域作为研究区,进行了无人机遥感建筑物震害提取,基于遥感震害指数进行了震害定量评估,并与现场建筑物震害调查统计结果进行了比较验证。结果显示,遥感解译建筑物震害与实际震害程度相吻合,表明利用震后快速获取的高分辨率无人机影像,可以较为准确地识别建筑物震害,进而为地震灾害定量评估和应急救援辅助决策提供重要参考。     

遥感影像空间分辨率变化对湖泊水体提取精度的影响

湖泊面积是表征湖泊水情变化的重要指示因子,如何从不同空间分辨率遥感数据中获取客观准确的水面信息,是当前遥感应用研究中的难点问题.本文以鄱阳湖为例,通过选用丰水期和枯水期代表性Landsat ETM+遥感影像,采用最邻近法(NN)和像元聚合法(PA)两种重采样方法,分别获取分辨率逐渐降低的不同分辨率的影像数据,结合归一化差异水指数法研究水域面积随遥感影像分辨率降低的变化趋势及其误差变化特征,同时深入分析不同影响因素对水体提取精度的差异.研究结果表明:(1)空间分辨率是影响鄱阳湖水体提取精度的重要因素之一,随着遥感影像空间分辨率的降低,提取水域面积的精度相对30 m分辨率时呈逐渐降低的趋势,但整体精度较高,最低精度在67.64%以上;(2)NN重采样方法对遥感影像波段亮度值的均值影响不大,但PA重采样后影像的均值和标准差随分辨率逐渐降低且变化更有规律;(3)水体阈值在PA重采样后变化较大,NN重采样后变化较小,因而采用30 m分辨率时获取的阈值提取PA重采样后鄱阳湖水体误差较大,提取NN重采样后的湖泊水体误差较小.本研究结果对于全球变化影响下湖泊水体信息遥感精确提取具有重要的参考价值.     

城市地震压埋人员分布评估研究——以天津市区为例

地震压埋人员分布是震后科学、有效地开展应急处置和救援的重要依据。针对地震中建筑物破坏造成的人员压埋,从人口分布和在室率着手,在社区级空间尺度上分时段建立人口数量、在室率和建筑物的对应关系;结合建筑物震害评估方法,构建地震压埋人员快速评估模型,并预评估天津市区各烈度下地震压埋人口的数量和分布情况。结果显示,夜间是发生地震压埋的高风险时段,住宅、大学、居民服务类和卫生医疗建筑是地震压埋高发区域,工作日白天应重点关注中小学。     

面向地震应急的人口数据精度评价

人员伤亡评估是实际地震应急工作中最重要的内容之一,而不同精度的人口数据对震后快速评估人员伤亡影响很大。以汶川地震为例,研究甘肃陇南地区不同精度的人口数据,基于GIS平台以高精度的居民地单元人口数据为参考,比较居民点单元、行政村单元、乡镇单元、区县单元四种空间化方法人口数据,对各种人口数据进行精度评价。研究结果表明:随着烈度区面积的减小,不同空间化方法获得的人口数据误差有变大的趋势,人口数据中行政单元越大,误差越大;区县单元人口空间化与居民地人口空间化数据误差最大,相对误差约为144.78%;居民点密度空间化人口数据误差最小,相对误差为15.38%。汶川地震甘肃陇南地区万人死亡率计算结果中,区县单元人口空间化数据计算得到的万人死亡率与居民地人口数据误差最大,绝对误差为5.31,居民点单元人口空间化数据计算得到的万人死亡率与居民地人口数据误差最小,绝对误差为0.70。     

LM-BP神经网络在震后建筑损失评估模型中的应用

当前震后建筑经济损失评估模型得到的震后建筑经济损失评估精确度、效率低,针对单一神经网络易产生局部极值等问题,对神经网络方法进行了改进,提出LM-BP神经网络在震后建筑损失评估模型中的应用。输入样本要素为影响震后建筑经济损失的5项因素,输出样本是震后建筑经济损失评估结果,在此基础上采用LM-BP神经网络将训练转化成最小二乘问题,结合LM算法重新定义隐含层节点数量,构建基于LM-BP的神经网络震后经济损失评估模型,采用该模型获取最优震后建筑经济损失评估结果。仿真实验结果表明,所设计的评估模型最小评估误差为0.1%,相比同类模型具有高精确度的优势,是一种可靠的震后建筑经济损失评估模型。     

面向防震减灾的人口数据空间化研究——以2007年宁洱地震灾区为例

入口是地震灾害的重要受灾体,准确的入口空问分布信息是防震减灾工作的重要依据.本文借助地理信息系统,将人口统计数据与高分辨率遥感数据相结合,应用基于居民地的人口数据空间化方法,模拟人口空间分布.首先根据城市人口—面积异速生长模型的分形几何意义,推导出城乡人口一面积统一模型;进而以2007年宁洱地震灾区为例,在建立居民地分类体系和遥感解译标志的基础上,目视解译获得准确的居民地信息;最后应用城乡人口—面积统一模型获得网格人口密度矢量数据.经检验,本文的结果达到了较高的精度.同时在人口数据空间化完成的基础上,以地震受灾人口估算为例,探讨了人口数据空间化在防震减灾中的应用.研究结果表明,基于网格人口矢量数据的受灾人口估算结果更能客观反映地震灾情,可以为防震减灾和应急救援工作提供可靠的依据.     

泸定Ms6.8地震房屋损失快速评估

震后房屋损失的快速评估对于灾后应急救援等至关重要。现有的地震风险评估方法要么仅提供损失的均值,要么以某一方差常数来描述损失的分布特征,均无法准确有效地反映各空间位置点损失的随机性及相关关系,最终影响整体损失评估结果的准确度。本文基于Copula理论,提出了一种适用于地震巨灾风险分析的相关随机变量模拟方法,好处是在实现快速计算的同时,能够考虑地震损失中的不确定性与相关性。利用所提方法对2022年9月5日四川泸定6.8级地震的房屋损失进行评估,得到了各结构类型与县区的损失分布,并与PAGER方法所得到的损失分布进行对比。结果表明：此次地震房屋总体损失超过89.8%的概率处于10～100亿元人民币量级水平,其中超过50.8%的概率为20～50亿元人民币;损失较大的三个县区分别是泸定县、石棉县和荥经县,砌体结构的经济损失约是框架结构的2倍;相比于PAGER,该方法给出的损失概率分布形状更加灵活,能够详细地反映不同县区的房屋损失特征。研究方法和结果为震后损失快速评估技术提供参考,也为未来地震的灾后应急救援等提供科学依据。     

STUDY ON THE MRF-BASED METHOD FOR DAMAGED BUILDINGS EXTRACTION FROM THE SINGLE-PHASE SEISMIC IMAGE

Earthquake events are one of the most extraordinarily serious natural calamities, which not only cause heavy casualties and economic losses, but also various secondary disasters. Such events are devastating, and have far-reaching influences. As the main disaster bearing body in earthquake, buildings are often seriously damaged, thus it can be used as an important reference for earthquake damage assessment. Identifying damaged buildings from post-earthquake images quickly and accurately is of real importance, which has guidance meaning to rescue and emergency response. At present, the assessment of earthquake damage is mainly through artificial field investigation, which is time-consuming and cannot meet the urgent requirements of rapid emergency response. Markov Random Field(MRF)combines the neighborhood system of pixels with the prior distribution model to effectively describe the dependence between spatial pixels and pixels, so as to obtain more accurate segmentation results. The support vector machine(SVM)model is a simple and clear mathematical model which has a solid theoretical basis; in addition, it also has unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition problems. Thus, in this paper, a Markov random field-based method for damaged buildings extraction from the single-phase seismic image is proposed. The framework of the proposed method has three components. Firstly, Markov Random Field was used to segment the image; then, the spectral and texture features of the post-earthquake damaged building area are extracted. After that, Support Vector Machine was used to extract the damaged buildings according to the extracted features. In order to evaluate the proposed method, 5 areas in ADS40 earthquake remote sensing image were selected as experimental data, this image covers parts of Wenchuan City, Sichuan Province, where an earthquake had struck in 2008. And in order to verify the applicability of this method to different resolution images, an experimental area was selected from different resolution images obtained by the same equipment. The experimental results show that the proposed method has good performance and could effectively identify the damaged buildings after the earthquake. The average overall accuracy of the selected experimental areas is 93.02%. Compared with the result extracted by the widely used eCognition software, the proposed method is simpler in operation and can improve the extraction accuracy and running time significantly. Therefore, it has significant meaning for both emergency rescue work and accurate disaster information providing after earthquake.     

基于格网的全国尺度地震灾害损失预测系统设计与实现

空间格网数据相比于矢量数据具有运算速度快、处理简单的特点,适合地震灾害损失震前预测或震后快速评估。但地震损失评估涉及地震危险性及人口、房屋建筑及其地震易损性等不同类型数据在全国范围内的千米格网分布,数据量大,数据变化时形成新的格网数据的工作量较大,使用常规震害预测算法会影响评估效率。依据地震损失评估原理,采取前置确定性损失评估策略和算法优化,结合GIS功能设计并编程实现了具有风险评估相关数据千米格网化处理、地震损失预测与震后快速评估等核心功能的软件系统。利用该系统进行了2016～2025年中国大陆千米格网地震损失预测,结果表明评估效率显著提高,该系统为我国新一代地震重点监视防御区的确定提供了实用化的震害损失预测工具,同时,在地震损失快速评估中亦得到较好应用。     

A GIS-based earthquake damage assessment and settlement methodology

Earthquakes have a greater effect on society than most people think. These effects range from structural damages to economic impacts and fatalities. An earthquake only lasts for a few seconds and the aftershocks may continue for days, but the damage does continue for years. Residential site safety and earthquake damage assessment studies play a crucial role in developing reliable rehabilitation and development programs, improving preparedness and mitigating losses in urbanized areas. The extremely densely populated metropolis of Tehran, which totals of 7,768,561 for 22 districts (according to the 2006 population census), coupled with the fragility of houses and infrastructure, highlight the necessity of a reliable earthquake damage assessment based on essential datasets, such as building resistance attributes, building population, soil structures, streets network and hazardous facilities. This paper presents a GIS-based model for earthquake loss estimation for a district in Tehran, Iran. Damages to buildings were calculated only for the ground shaking effect of one of the region's most active faults, the Mosha Fault in a likely earthquake scenario. Earthquake intensity for each building location was estimated based on attenuation relation and the ratio of damage was obtained from customized fragility curves. Human casualties and street blockages caused by collapsed buildings were taken into account in this study, as well. Finally, accessibility verification found locations without clear passages for temporary settlements by buildings via open streets. The model was validated using the 2003 Bam earthquake damages. The proposed model enables the decision-makers to make more reliable decisions based on various spatial datasets before and after an earthquake occurs. The results of the earthquake application showed total losses as follows: structural damages reaching 64% of the building stock, a death rate of 33% of all the residents, a severe injury rate reaching 27% of the population and street closures upwards of 22% due to building collapse.     

A PRELIMINARY STUDY ON THE METHOD OF SEISMIC INTENSITY ASSESSMENT BASED ON RESIDENTIAL BUILDING DATA AND HIGH RESOLUTION REMOTE SENSING IMAGES

After destructive earthquakes, the assessment result of seismic intensity is an important decision-making basis for emergency rescue, recovery and reconstruction. This job requires higher timeliness by government and society. Because remote sensing technology is not affected by the terrible traffic conditions on the ground after the earthquake, large-scale seismic damage information in the earthquake area can be collected in a short time by the remote sensing image. The remote sensing technique plays a more and more important role in rapid acquisition of seismic damage information, emergency rescue decision-making, seismic intensity assessment and other work. On the basis of previous studies, this paper proposes a new method to assess seismic intensity by using remote sensing image, i.e. to interpret the building collapse rate of a residential quarter after an earthquake by high-resolution remote sensing images. If there already are detailed building data and building structure vulnerability matrix data of a residential area, we can calculate the building collapse rate under any intensity values in this residential area by using the theory of earthquake damage prediction. Assuming that the building collapse rate interpreted by remote sensing is equal to the building collapse rate predicted by using the existing data, it will be easy to calculate the actual seismic intensity of the residential area in this earthquake event. Based on this idea, according to the relevant standard specifications issued by China Earthquake Administration, this paper puts forward some functional models, such as the calculation model of building collapse rate based on remote sensing, the data matrix model of residential building structure, the prediction function matrix model of residential building collapse rate and the prediction model of residential building collapse rate. A formula for calculating seismic intensity by using remote sensing interpretation of collapse rate is also proposed. To test and verify the proposed method, this paper takes two neighboring blocks of Jiegu Town after the Yushu M7.1 earthquake in Qinghai Province as an example. The building structure matrix of the study block was constructed by using pre-earthquake 0.6m resolution satellite remote sensing image(QuickBird, acquired on November 6, 2004), post-earthquake 0.2m aerial remote sensing image(acquired by National Bureau of Surveying and Mapping, April 15, 2010) and some field investigation data. The building collapse rate in the two blocks was calculated by using the interpretation results of seismic damage from the Remote Sensing Technology Coordinating Group of China Seismological Bureau. The seismic damage matrix of building structures in Yushu area is constructed by using the abundant scientific data of the scientific investigation team of the project “Comprehensive Scientific Investigation of the Yushu M7.1 Earthquake in Qinghai Province” of China Seismological Bureau. On this basis, the collapse rate prediction function of different structures in Yushu area is constructed. According to the prediction function of collapse rate and the building structure matrix of the two blocks, the building collapse rate under different intensity values is predicted, and the curve of intensity-collapse rate function is drawn. By comparing the building collapse rate interpreted by remote sensing and the intensity-collapse rate function curve of this two blocks, the seismic intensity of both blocks are calculated to be the same value: Ⅸ degree, which is consistent with the results of the field scientific investigation of the earthquake. The validation shows that the method proposed in this paper can effectively avoid the influence caused by the difference of seismic performance of buildings and accurately evaluate seismic intensity when using remote sensing technique. The method has certain application value for earthquake emergency work.     

基于GIS的震后修复建筑物空间分布格网化方法研究

针对传统格网化方法存在计算误差较大、计算效率较低等问题,提出一种新的方法——基于GIS的震后修复建筑物空间分布格网化方法。利用DEM数据、土地数据以及基础地理数据等,提取地形、地貌等各种影响因子。利用建筑面积总和以及土地利用分类数据进行多元线性回归建模,获取50 mm格网尺度下的建筑分布,利用POI数据与建筑空间的相关性,引入四叉树获取最高中心点集,在上述基础上构建格网震后修复建筑物空间分布模型,完成震后修复建筑物空间分布。实验结果表明,所提方法可有效降低计算误差,提高计算效率。