• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.25-32
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• 2005

This paper presents a method for point-based 3D building reconstruction using Lidar data and digital map. The proposed method consists of three processes: extraction of building roof points, identification of roof types, and 3D building reconstruction. After extracting points inside the polygon of building, the ground surface, wall and tree points among the extracted points are removed through the filtering process. The filtered points are then fitted into the flat plane using ODR(Orthogonal Distance Regression) in the first place. If the fitting error is within the predefined threshold, the surface is classified as a flat roof. Otherwise, the surface is fitted and classified into a gable or arch roof through RMSE analysis. Experimental results showed that the proposed method classified successfully three different types of roof and that the fusion of LIDAR data and digital map could be a feasible method of modeling 3D building reconstruction.

• Korean Journal of Remote Sensing
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• v.19 no.4
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• pp.307-317
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• 2003

This paper presents an algorithm that automatically extracts buildings among many different features on the earth surface by fusing LIDAR data with panchromatic aerial images. The proposed algorithm consists of three stages such as point level process, polygon level process, parameter space level process. At the first stage, we eliminate gross errors and apply a local maxima filter to detect building candidate points from the raw laser scanning data. After then, a grouping procedure is performed for segmenting raw LIDAR data and the segmented LIDAR data is polygonized by the encasing polygon algorithm developed in the research. At the second stage, we eliminate non-building polygons using several constraints such as area and circularity. At the last stage, all the polygons generated at the second stage are projected onto the aerial stereo images through collinearity condition equations. Finally, we fuse the projected encasing polygons with edges detected by image processing for refining the building segments. The experimental results showed that the RMSEs of building corners in X, Y and Z were 8.1cm, 24.7cm, 35.9cm, respectively.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.3 s.37
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• pp.13-22
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• 2006

Realistic 3D building construction in urban area has become an important issue because of increasing demand of 3D geo-spatial information in many application. Contrary to the conventional 3D building model construction approach using aerial images and high-resolution satellite imagery, it has been researched widely in building reconstruction using high-accuracy aerial LIDAR data in the latest. This paper presents a method for 3D building construction through building outlines extraction by LoG operator's Zero-crossing and line generation and refinement by Douglas-Peucker algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.163-171
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• 2012

High resolution satellite images have been used for building and road system analysis, landscape analysis, and ecological assessment for several years. However, in high resolution satellite images, shadows are necessarily cast by manmade objects such as buildings and over-pass bridges. This paper develops the shadow extraction procedures in urban area including various land-use classes, and the extracted shadow areas are evaluated by a manually digitized shadow map. For the shadow extraction, the Canny edge operator and the dilation filter are applied to make building edge buffer area. Also, the object-based segmentation was performed using Gram-Schmitt fusion image, and spectral and spatial parameters are calculated from the segmentation results. Finally, we proposed appropriate parameters and extraction rules for the shadow extraction. The accuracy of the shadow extraction results from the various assessment indices is 80% to 90%.

• Annual Conference of KIPS
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• 2011.11a
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• pp.238-240
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• 2011

수많은 건물들이 세워지고 있는 가운데 안전 역시 중요한 문제로 인식되고 있다. 본 논문은 유비쿼터스 시대에 맞추어 센서네트워크를 이용하여 건물의 화재정보를 실시간으로 감지하고 화재 발생 시 다양한 상황정보를 고려하여 대피 경로를 사용자 스마트 기기로 알려주는 상황정보 기반의 지능형 화재대피 서비스 모델 설계를 제안한다. 제안하는 서비스 모델은 센서가 실시간으로 건물 내부를 감지하고 화재 여부 및 화재지역, 연기량, 사람수 등의 상황정보를 파악하여 서버에 전송하고 서버는 전송된 정보를 분석하여 대피경로 추출 알고리즘을 이용해 최적의 대피 경로를 사용자의 스마트기기로 전송한다. 이 시스템 구현으로 건물 화재 발생시 사람들이 안전하게 대피할 수 있을 것으로 사료된다.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.549-554
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• 2008

도시와 같은 광범위한 영역에 대한 지형, 지물의 기하 정보를 추출 또는 복원하는 기술은 공간 영상정보 시스템을 비롯한 다양한 응용분야에서 사용되고 있으며, 이러한 필요에 따라 중요성이 더욱 커지고 있다. 본 논문은 위성 영상에서 건물의 footprint와 rooftop, 그림자 정보를 이용하여 건물을 반 자동으로 모델링하고, 이를 지형에 정합하는 시스템을 제안한다. 제안하는 시스템은 사용자의 직접적인 조작과 자동으로 이루어는 부분을 조합하여, 최소한의 사용자 조작으로 건물을 모델링하고, 지형에 의한 요소를 고려하여 건물의 실제 위치를 보정하여 지형과 정합을 수행한다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2006.05a
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• pp.138-145
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• 2006

Recently the extraction of building height information has been investigated using remotely sensed image and digital maps. In this study, based on the digital photogrammetry principle and mono imagery method the building height information can be extracted by using relationship between ground coordinates and image coordinates, To evaluate the result the comparison was done with building height from 1:5000 aerial photo. The experiment shows that extraction of building height could be performed using IKONOS single imagery and digital map and it is proved that the building height could be reconstructed within some extent.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.227-237
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• 2022

The most critical factors for detecting changes in very high-resolution satellite images are building positional inconsistencies and relief displacements caused by satellite side-view. To resolve the above problems, additional processing using a digital elevation model and deep learning approach have been proposed. Unfortunately, these approaches are not sufficiently effective in solving these problems. This study proposed a change detection method that considers both positional and topology information of buildings. Mask R-CNN (Region-based Convolutional Neural Network) was trained on a SpaceNet building detection v2 dataset, and the central points of each building were extracted as building nodes. Then, triangulated irregular network graphs were created on building nodes from temporal images. To extract the area, where there is a structural difference between two graphs, a change index reflecting the similarity of the graphs and differences in the location of building nodes was proposed. Finally, newly changed or deleted buildings were detected by comparing the two graphs. Three pairs of test sites were selected to evaluate the proposed method's effectiveness, and the results showed that changed buildings were detected in the case of side-view satellite images with building positional inconsistencies.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.355-361
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• 2005

3차원 자료의 필요에 발맞추어 3차원 좌표를 직접적으로 획득할 수 있는 LIDAR 시스템이 등장하게 되었다 항공 LIDAR 시스템은 항공기, GPS, INS, Laser Scanner가 통합된 시스템으로 항공기에서 발사된 Laser의 반사파를 이용하여 거리와 그 때의 항공기의 자세, 위치를 통합하여 직접적인 3차원 포인트 자료를 획득할 수 있다. LiDAR 데이터는 지형, 건물, 식생 등의 지면위에 있는 모든 객체에 대한 3차원 자료와 영상자료를 함께 제공하고 있다. 이러한 LIDAR 자료로부터 DEM, DTM 등의 지형 정보와 식목, 건물 등 지물정보를 추출하는 연구가 활발하게 이루어지고 있다. 본 연구에서는 지형을 추출하는데 사용할 수 있는 몇 가지 필터링기법을 선정하여 국내의 다양한 지모, 지물에 적용하고 그 정확도를 평가해 보았다.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.135-142
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• 2014

As interest in social safety has recently increased at the national level, the various activities which can effectively prevent crimes are being carried out. Because the existing maps related to crimes provide the information about the present condition of crimes by administrative district for users, women and pedestrians who go by night could not actually grasp safe roads in advance. Therefore, this study developed the methodology that can easily extract dangerous areas due to crimes by the digital map 2.0. In the digital map 2.0, location and attribute information of center-lines of roads and building layers were used to find dangerous areas of crimes in these layers. Pavement materials and road width which are already built by the attribute information were used in the center-lines of roads. Crossing angles that roads and roads cross each other were additionally extracted and utilized. The attribute information about building types were input in the building layers of the digital map 2.0. The areas that are more the threshold values set by totaling up all the risk scores when considering pavement materials, road width, crossing angles of road, and building types in the center-lines of roads and road crossings were extracted as the dangerous areas that crimes can occur. Verification of the developed methodology was done by experiment. In the spatial apsect, the dangerous areas of crimes could be found by using the digital 2.0, roads, and building layers only through the experiment. In the administrative aspect to prevent crimes, additional installation of safety facilities such as street lights and security lights in the identified areas which are vulnerable for crimes is thought to be increasing safety of dangerous areas.