• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.296-300
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• 2009

가상현실이나 인터넷 웹지도 서비스와 같이 3차원의 실세계를 시스템 상에 그대로 재현(reconstruction)하기 위해서는 정교하고 세밀한 3차원 도시모델이 필수적이다. 이러한 3차원 도시모델의 자동생성은 원격탐사 및 사진측량 분야에서 많은 연구가 수행되고 있다. 이러한 연구들은 다양한 센서 데이터와 기 구축되어 있는 GIS자료를 이용하여 건물, 도로, 지형 등의 도시모델을 자동으로 생성하고자 한다. 그러나 대부분의 연구에서 추출한 각 기본요소(primitives)-평면패치(planar patches), 에지(edges), 모서리(corners)에 대한 국부적인 정제(refinement)는 수행하였으나, 생성한 건물 모델에 대한 광역적인 조정을 통한 정규화에 대한 연구는 미비한 상태이다. 본 연구에서는 다양한 데이터로부터 생성된 B-rep (boundary representation) 형태의 건물 모델에 대하여 기하학적인 제약요소(constraints)를 이용한 정규화(regularization) 방법론을 제시하고자 한다. 제안하는 방법은 건물의 Domain Knowledge에 기반하여 도출한 건물을 구성하는 기본요소(primitives)간의 인접성, 직교성, 평행성, 교차성 등의 다양한 제약조건을 이용하여 광역적으로 조정한다. 시뮬레이션 데이터에 적용한 결과의 분석을 통해 제안된 정규화 방법을 통해 오차가 포함된 건물모델이 보다 정형화된 형태로 조정되었음을 확인하였다.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.97-106
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• 2020

The factory information provided by the Industrial Location Information System (ILIS) is provided as raw data by the Korea Industrial Complex Corporation and registered after a filtering process, so the new factory information update is slow. In this study, to solve the problem of updating factory information of industrial location information system, using building data of road name address with relatively fast renewal cycle and building data of real estate, we compared the factory information of existing ILIS and extracted new factory information. In the process of comparison, a method was proposed to compare spatial objects of different types with point data and polygon data. Attribute information matching and object matching were performed, and attribute values of new factory information were extracted. The accuracy evaluation of the proposed spatial analysis method showed 79% accuracy, and the above matching technique was used to confirm the possibility of convergence of road name address data, real estate data and factory information of ILIS.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.503-514
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• 2016

Delineation of accurate object boundaries is crucial to provide reliable spatial information products such as digital topographic maps, building models, and spatial database. In LiDAR(Light Detection and Ranging) data, real boundaries of the buildings exist somewhere between outer-most points on the roofs and the closest points to the buildings among points on the ground. In most cases, areas of the building footprints represented by LiDAR points are smaller than actual size of the buildings because LiDAR points are located inside of the physical boundaries. Therefore, building boundaries determined by points on the roofs do not coincide with the actual footprints. This paper aims to estimate accurate boundaries that are close to the physical boundaries using airborne LiDAR data. The accurate boundaries are determined from the non-gridded original LiDAR data using initial boundaries extracted from the gridded data. The similar method implemented in this paper is also found in demarcation of the maritime boundary between two territories. The proposed method consists of determining initial boundaries with segmented LiDAR data, estimating accurate boundaries, and accuracy evaluation. In addition, extremely low density data was also utilized for verifying robustness of the method. Both simulation and real LiDAR data were used to demonstrate feasibility of the method. The results show that the proposed method is effective even though further refinement and improvement process could be required.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.4
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• pp.405-412
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• 2012

After the spatial discrepancy between SLI(Street-Level Imagery) and vector map is removed by their conflation, the corresponding building regions can be found based on SLI parameters. The building region correspondence, however, is not perfect even after the conflation. This paper aims to improve the correspondence of building regions by region splitting of an SLI. Regions are initialized by the seed lines, projection of building objects onto SLI scene. First, sky images are generated by filtering, segmentation, and sky region detection. Candidates for split lines are detected by edge detector, and then images are splitted into building regions by optimal split lines based on color difference and sky existence. The experiments demonstrated that the proposed region splitting method had improved the accuracy of building region correspondence from 83.3% to 89.7%. The result can be utilized effectively for enhancement of SLI services.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.550-555
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• 2003

최근에 Lidar 데이터를 이용한 3차원 위치 정보와 지표면 속성 정보를 취득하는 연구가 많이 진행되고 있다. 높은 위치 정확도, 3차원 데이터 동시 취득, 기존 측정 방식에 비하여 점 데이터 취득의 자동화, 데이터 정확도의 안정성 등으로 인하여 복잡한 지형 및 인공구조물이 존재하는 지역에서 Lidar 데이터의 응용 사례가 많이 나타나고 있으며, 특히 건물 모델링에서 반자동 방식의 디지털 사진측량에 비하여 자동 모델링의 가능성을 보여주고 있다. 일반적으로 Lidar 데이터의 점밀도는 1점/㎡이내이며, 촬영된 스트립을 중복시켜 점밀도를 높이기도 한다. 건물은 크기와 형태가 다양하기 때문에 모델링에 필요한 점밀도를 제시하기는 어렵지만 5점 내외에서 모델링이 가능하다고 알려져 있으며 건물이외에 다른 지형지물에 대한 모델링 연구는 거의 이루어지지 않고 있다. 따라서 본 논문에서는 Lidar 데이터의 점밀도에 따라 지물의 모델링 가능성을 평가하고 효율적인 데이터 취득 방안을 제시하고자 한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.619-628
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• 2009

LiDAR has capability of rapid data acquisition and provides useful information for reconstructing surface of the Earth. However, Extracting information from LiDAR data is not easy task because LiDAR data consist of irregularly distributed point clouds of 3D coordinates and lack of semantic and visual information. This thesis proposed methods for automatic extraction of buildings and 3D detail modeling using airborne LiDAR data. As for preprocessing, noise and unnecessary data were removed by iterative surface fitting and then classification of ground and non-ground data was performed by analyzing histogram. Footprints of the buildings were extracted by tracing points on the building boundaries. The refined footprints were obtained by regularization based on the building hypothesis. The accuracy of building footprints were evaluated by comparing with 1:1,000 digital vector maps. The horizontal RMSE was 0.56m for test areas. Finally, a method of 3D modeling of roof superstructure was developed. Statistical and geometric information of the LiDAR data on building roof were analyzed to segment data and to determine roof shape. The superstructures on the roof were modeled by 3D analytical functions that were derived by least square method. The accuracy of the 3D modeling was estimated using simulation data. The RMSEs were 0.91m, 1.43m, 1.85m and 1.97m for flat, sloped, arch and dome shapes, respectively. The methods developed in study show that the automation of 3D building modeling process was effectively performed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.545-554
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• 2019

This research aims to provide a building area extraction approach over the areas where data acquisition is impossible through field surveying, aerial photography and lidar scanning. Hence, high-resolution satellite images, which have high accessibility over the earth, are utilized for the automated building extraction in this study. 3D point clouds or DSM (Digital Surface Models), derived from the stereo image matching process, provides low quality of building area extraction due to their high level of noises and holes. In this regards, this research proposes a hybrid building area extraction approach which utilizes 3D point clouds (from image matching), and color and linear information (from imagery). First of all, ground and non-ground points are separated from 3D point clouds; then, the initial building hypothesis is extracted from the non-ground points. Secondly, color based building hypothesis is produced by considering the overlapping between the initial building hypothesis and the color segmentation result. Afterwards, line detection and space partitioning results are utilized to acquire the final building areas. The proposed approach shows 98.44% of correctness, 95.05% of completeness, and 1.05m of positional accuracy. Moreover, we see the possibility that the irregular shapes of building areas can be extracted through the proposed approach.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.4
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• pp.327-336
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• 2007

The 3D building modeling is one of crucial components in building 3D geospatial information. The existing methods for 3D building modeling depend mainly on manual photogrammetric processes by stereoplotter compiler, which indeed take great amount of time and efforts. In addition, some automatic methods that were proposed in research papers and experimental trials have limitations of describing the details of buildings with lack of geometric accuracy. It is essential in automatic fashion that the boundary and shape of buildings should be drawn effortlessly by a sophisticated algorithm. In recent years, airborne LiDAR data representing earth surface in 3D has been utilized in many different fields. However, it is still in technical difficulties for clean and correct boundary extraction without human intervention. The usage of airborne LiDAR data will be much feasible to reconstruct the roof tops of buildings whose boundary lines could be taken out from existing digital maps. The paper proposed a method to reconstruct the roof tops of buildings using airborne LiDAR data with building boundary lines from digital map. The primary process is to perform octree-based segmentation to airborne LiDAR data recursively in 3D space till there are no more airborne LiDAR points to be segmented. Once the octree-based segmentation has been completed, each segmented patch is thereafter merged based on geometric spatial characteristics. The experimental results showed that the proposed method were capable of extracting various building roof components such as plane, gable, polyhedric and curved surface.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.47-56
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• 2010

The use of airborne LiDAR data obtained by airborne laser scanners has increased in the field of spatial information such as building modeling. LiDAR data consist of irregularly distributed 3D coordinates and lack visual and semantic information. Therefore, LiDAR data processing is complicate. This study suggested a method of LiDAR data segmentation using roof surface patches from aerial images. Each segmented patch was modeled by analyzing geometric characteristics of the LiDAR data. The optimal functions could be determined with segmented data that fits various shapes of the roof surfaces as flat and slanted planes, dome and arch types. However, satisfiable segmentation results were not obtained occasionally due to shadow and tonal variation on the images. Therefore, methods to remove unnecessary edges result in incorrect segmentation are required.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.4-6
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• 2012

본 논문에서는 신속하고 정확하게 건물의 외곽선을 추출하기 위해서 항공 라이다 데이터를 타일 단위지형 분류 기법을 이용하여 분류하고, 건물 관련 타일의 항공영상으로부터 에지 정보를 추출하여 정확하게 건물 외곽선을 추출하는 기법을 제안한다. 제안된 건물 외곽선 추출 기법에서는 대부분의 연산을 타일 단위로 수행하고 항공영상의 특징 추출 범위를 건물 영역에 집중시킴으로써 건물의 외곽선을 정확하게 추출하는 과정의 처리속도를 개선하였다.