[논문]몬테카를로 적분을 통한 3차원 점군의 건물 식별기법 연구

이채연; 안승만

doi:10.11108/kagis.2020.23.4.016

[국내논문] 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 원문보기

한국지리정보학회지 = Journal of the Korean Association of Geographic Information Studies, v.23 no.4, 2020년, pp.16 - 41

초록
AI-Helper

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.

Abstract ▼ AI-Helper

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.

Keyword

표/그림 (14)

그림 FIGURE 1. Location and size of the study site and classified 3DPC land-cover features of the site: (a) domain area of D1 and D2; (b) classified 3DPC in D1; (c) classified 3DPC in D2.
그림 FIGURE 2. Stepwise 3DPC classification and accuracy assessment process used in this study: (a) accuracy assessment scheme (b) a multi-step 3DPC classification process; (c) assessment of each 3DPC land cover classification accuracy and total land cover classification accuracy.
그림 FIGURE 3. Four by four sampling framework for land-cover identification from 3DPC data contained in grid cells.
표 TABLE 1. The point numbers of the used 3DPCs
그림 FIGURE 4. Conceptual illustration of 3DPC- and area based SP calculation; (a) introducing circles with radii equal to search-distance, centered around 3DPC data points in the delimited rectangular cell area; (b) counting of total counts of random points in these circles; (c) calculation of SP as area from total counts of random points in circles.
그림 FIGURE 5. Flowchart of the 3DCP based land-cover SP definition and identification: (a) preprocessing of raw 3DPC data needed for land-cover classification; (b) SP definition of cells from calculation using equation (1)–(4); (c) initial land-cover identification using default configuration (SP threshold of 0.5, P_0.5); (d) the land-cover identification in the user-defined spatial sampling framework (m_u × n_u array); (e) resolution GUI; and (f) SP threshold GUI (50 means P_0.5).
그림 FIGURE 6. The variation in the geometric shapes of the identified buildings (D2) generated from the six different sampling resolutions (1-, 2-, 5-, 10-, 15-, and 20-m), the five different SP thresholds (P_0.1, P_0.3, P_0.5, P_0.7, and P_0.9) and the six different 3DPC densities (1/1, 1/2, 1/5, 1/10, 1/15, and 1/20). Shown for: (a) 1-m resolution; (b) 2-m resolution; (c) 5-m resolution; (d) 10-m resolution; (e) 15-m resolution; and (f) 20-m resolution.
그림 FIGURE 7. Areal proportions of land cover identified as built structure in D2 with different resolutions (1-, 2-, 5-, 10-, 15-, and 20-m) and SP thresholds (P_0.1, P_0.3, P_0.5, P_0.7, and P_0.9). Shown for: (a) 3DPC density of (1/1); (b) 3DPC density of (1/2); (c) 3DPC density of (1/5); (d) 3DPC density of (1/10); (e) 3DPC density of (1/15); and (f) 3DPC density of (1/20).
표 TABLE 2. Summary of land-cover area identified as built structure and bias compared to the reference (in D2)
그림 FIGURE 8. Spatial distributions of SP in D1 model area. SP grids were generated at six resolutions (1-, 2-, 5-, 10-, and 20-m) for each of six 3DPC densities (1/1, 1/2, 1/5, 1/10, 1/15, and 1/20).
그림 FIGURE 9. Variation of areal proportions of built structure in D1. Raster grids were generated at five resolutions (1-, 2-, 5-, 10-, 15-, and 20-m) for each of the six probability densities (P_0.1, P_0.3, P_0.5, P_0.7, and P_0.9).
표 TABLE 3. Areal proportions of land cover identified as built structure and bias (in D1)
그림 FIGURE 10. Spatial distributions of registered building grids in D1, generated by four different resolutions (2-, 5-, 10-, and 20-m) and five different probability densities (P_0.1, P_0.3, P_0.5, P_0.7, and P_0.9).
그림 FIGURE 11. Building land-cover SP areal proportions in D1 (SP>0) and mean SPs in D1. Building land-cover SPs were calculated at six resolutions (1-, 2-, 5-, 10-, and 20-m) for each of six 3DPC densities (1/1, 1/2, 1/5, 1/10, 1/15, and 1/20).

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