测绘通报 ›› 2022, Vol. 0 ›› Issue (11): 118-122.doi: 10.13474/j.cnki.11-2246.2022.0336

• 技术交流 • 上一篇    下一篇

结合空间位置和语义匹配的多源房屋灾害数据融合

倪慧珠1, 万术海1, 司涵1, 冯思园1, 任晓磊1, 李正强1, 卢涛2   

  1. 1. 浙江省测绘科学技术研究院, 浙江 杭州 311100;
    2. 桐庐广隶土地勘测规划设计有限公司, 浙江 杭州 311599
  • 收稿日期:2021-11-29 修回日期:2022-08-26 发布日期:2022-12-08
  • 通讯作者: 万术海,E-mail:beneone@qq.com
  • 作者简介:倪慧珠(1980-),女,硕士生,主要从事测绘地理信息方面的工作。E-mail:15975350@qq.com

Multi-source building disaster information fusion based on spatial and semantic matching

NI Huizhu1, WAN Shuhai1, SI Han1, FENG Siyuan1, REN Xiaolei1, LI Zhengqiang1, LU Tao2   

  1. 1. Zhejiang Academy of Surveying and Mapping Science and Technology, Hangzhou 311100, China;
    2. Tonglu Guangli Land Survey Planning and Design Co., Ltd., Hangzhou 311599, China
  • Received:2021-11-29 Revised:2022-08-26 Published:2022-12-08

摘要: 自然灾害综合风险普查工作中,房屋灾害信息数据来源众多,为调查底库快速建库带来很大挑战。本文针对多源房屋数据难以快速提取融合的问题,基于地理空间位置的信息匹配和基于地理空间语义的信息匹配等技术,通过空间数据提取、房屋建筑增补绘、属性信息匹配、属性信息选择,实现了房屋建筑多源信息快速自动融合,为自然灾害普查工作提供了可靠翔实的房屋建筑数据底板。实践证明,该技术成果数据准确可靠,可有效提升自然灾害风险普查工作效率,大量降低人工成本。

关键词: 灾害普查, 空间数据提取, 属性信息匹配, 地理空间语义, 多源数据融合

Abstract: In the national survey on natural disaster risks, there are many sources of house disaster information data, which brings great challenges to the rapid construction of the investigation base database. In order to extract and fuse multi-source house data quickly, this paper realizes the rapid and automatic fusion and reliability evaluation of multi-source house information through the operations of basic data preprocessing, spatial data extraction and automatic fusion of attribute information by using technologies such as information matching based on geospatial location and information matching based on geospatial semantics. On this basis, the natural disaster census database building system is developed to provide reliable and detailed housing construction data for natural disaster census. Practice had proved that the platform results were accurate and reliable, which could effectively improve the efficiency of natural disaster risk survey and greatly reduce labor costs.

Key words: natural disaster census, spatial data extraction, attribute information matching, geospatial semantic, multi-source data fusion

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