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作 者:朱达邈 王东明 Zhu Damiao;Wang Dongming(China Earthquake Disaster Prevention Center,Beijing 100029,China)
出 处:《震灾防御技术》2021年第3期510-518,共9页Technology for Earthquake Disaster Prevention
基 金:国家重点研发计划(2019YFC1509401);地震科技星火计划项目(XH19058Y)。
摘 要: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。Building data is the basis of pre-assessment of key earthquake risk areas.Based on the obtained building information,pre-assessment of casualties,economic losses and relief material needs can be carried out.In the pre-assessment of key earthquake risk areas over the years,the proportion of building information obtained through field investigation is very small,so it can only be sampled.Therefore,in order to estimate the loss of all buildings in the dangerous area in batches,the best way is to obtain the vector data of buildings based on remote sensing images and establish a database.In order to achieve the rapid acquisition of large quantities of build-ing vector data in the pre-assessment of key seismic risk areas in China,this paper proposes a method of building spatial distribution data batch acquisition based on remote sensing images,and then obtains the building spatial vector data in the key seismic risk areas,combined with the building attribute information obtained from the field sampling survey,The spatial distribution database of buildings in key seismic risk areas is established to provide data basis for disaster loss pre assessment in key seismic risk areas.The method pro-posed in this paper can be widely used in the acquisition of building information in key seismic risk areas in the future,which can not only improve the work efficiency,but also reduce the cost,and improve the scientificity and accuracy of pre-assessment.
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