한국지리정보학회지 (Journal of the Korean Association of Geographic Information Studies)

  • 제23권4호
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  • Pages.16-41
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  • 2020
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  • 1226-9719(pISSN)
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  • 2287-6952(eISSN)
한국지리정보학회 (The Korean Association of Geographic Information Studies)
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몬테카를로 적분을 통한 3차원 점군의 건물 식별기법 연구

A Study on Building Identification from the Three-dimensional Point Cloud by using Monte Carlo Integration Method

  • YI, Chaeyeon (Hankuk University of Foreign Studies, Research Center for Atmospheric Environment) ;
  • AN, Seung-Man (Korea Research Institute for Human Settlements)
  • 투고 : 2020.10.12
  • 심사 : 2020.10.28
  • 발행 : 2020.12.31
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초록

실제 공간의 분포 또는 양적 속성을 대변하는 지리정보 입력은 지구시스템 모의 내에서 주요 관심사가 되고 있다. 많은 연구에서 다양한 격자 해상도에서의 지표면 특성에 대한 부정확한 추정이 모델링 결과를 크게 바꾸는 것으로 나타났다. 따라서, 이 논문은 도시지역 건물들의 분포와 면적·체적 속성을 반영하기 위해서, 항공라이다로 수집된 3DPC(three-dimensional point cloud) 샘플링 체계에 Monte Carlo Integration(MCI) 기법 기반 공간확률(spatial probability)을 적용을 제안하였다. 건물 식별과 관련해 공간확률(SP) 임계치, 격자 크기, 3차원점군 밀도 세 인자의 결정규칙 적용 결과가 비교되었다. 연구 결과, 건물의 격자가 커짐에 따라 식별되는 건물의 면적 속성이 증가하였다. 공간 모델링 및 분석의 신뢰성을 높이기 위해서는 샘플링 체계에서의 결정규칙을 사용하여 건물의 면적 속성을 조정하는 것이 권장된다. 제안된 방법은 모델링 분야가 요구하는 크고 작은 격자의 변화에서도 일정하게 건물 면적 속성이 유지되도록 지원할 것이다.

Geospatial input setting to represent the reality of spatial distribution or quantitative property within model has become a major interest in earth system simulation. Many studies showed the variation of grid resolution could lead to drastic changes of spatial model results because of insufficient surface property estimations. Hence, in this paper, the authors proposed Monte Carlo Integration (MCI) to apply spatial probability (SP) in a spatial-sampling framework using a three-dimensional point cloud (3DPC) to keep the optimized spatial distribution and area/volume property of buildings in urban area. Three different decision rule based building identification results were compared : SP threshold, cell size, and 3DPC density. Results shows the identified building area property tend to increase according to the spatial sampling grid area enlargement. Hence, areal building property manipulation in the sampling frameworks by using decision rules is strongly recommended to increase reliability of geospatial modeling and analysis results. Proposed method will support the modeling needs to keep quantitative building properties in both finer and coarser grids.

키워드

과제정보

이 연구는 기상청 <자연재해대응 영향예보 생산기술개발>(KMI2018-01410)의 지원으로 수행되었습니다.

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