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In order to meet the needs of rapid assessment of earthquake disasters and visual expression of spatial distribution of population, this paper takes Luding County of Ganzi Tibetan Autonomous Prefecture in Sichuan Province as the study area. The research employs an object-oriented classification method to extract the shadows of buildings from high-resolution remote sensing images, obtain the length of the shadows, and combine this data with building data of geographic conditions census to determine the number of floors in the building zones. Concurrently, seventh national population census data are integrated with building area to create a population data set based on buildings, thereby completing the high-resolution regional population data spatialization simulation. Furthermore, 50-m grid data is generated through data resampling, which markedly enhances the accuracy of the population grid data, allowing for more precise localization of the population distribution, thus proving advantageous for post-earthquake emergency response operations. The feasibility of this method has been demonstrated, and its potential for broader application is evident.

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Methodology for Spatialization of Regional Demographic Data Based on Remote Sensing and Geographic Conditions Census Data

  • 会议论文

  • 发表日期：

    2025年

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  • Yahui Chen,
  • Xiaoyue Gao,
  • Runliang He,
  • Yunzhi Zhang,
  • Chenfei Yu

摘要

In order to meet the needs of rapid assessment of earthquake disasters and visual expression of spatial distribution of population, this paper takes Luding County of Ganzi Tibetan Autonomous Prefecture in Sichuan Province as the study area. The research employs an object-oriented classification method to extract the shadows of buildings from high-resolution remote sensing images, obtain the length of the shadows, and combine this data with building data of geographic conditions census to determine the number of floors in the building zones. Concurrently, seventh national population census data are integrated with building area to create a population data set based on buildings, thereby completing the high-resolution regional population data spatialization simulation. Furthermore, 50-m grid data is generated through data resampling, which markedly enhances the accuracy of the population grid data, allowing for more precise localization of the population distribution, thus proving advantageous for post-earthquake emergency response operations. The feasibility of this method has been demonstrated, and its potential for broader application is evident.