• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.355-356
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• 2011

LiDAR data is very large, which contains an amount of redundant information. The information not only takes up a lot of storage space but also brings much inconvenience to the LIDAR data transmission and application. Therefore, a simplified method was proposed for LiDAR data based on quad-tree structure in this paper. The boundary contour lines of the buildings are displayed as building extraction. Experimental results show that the method is efficient for point's simplification according to the rule of mapping.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.530-535
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• 2002

An approach for quickly updating GIS building data using high resolution remotely sensed data is proposed in this paper. High resolution remotely sensed data could be aerial photographs, satellite images and airborne laser scanning data. Data from different types of sensors are integrated in building extraction. Based on the extracted buildings and the outdated GIS database, the change-detection-template can be automatically created. Then, GIS building data can be fast updated by semiautomatically processing the change-detection-temp late. It is demonstrated that this approach is quick, effective and applicable.

• Journal of the Korean Geophysical Society
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• v.9 no.1
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• pp.7-19
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• 2006

Laser scanning is a new technology for obtaining Digital Surface Models(DSM) of the earth surface.It is a fast method for sampling the earth surface with high density and high point accuracy. This paper is for buildings extraction from LiDAR points data. The core part of building construction is based on a parameters filter for distinguishing between terrain and non-terrain laser points. The 3D geometrical properties of the building facades are obtained based on plane fitting using least-squares adjustment. The reconstruction part of the procedure is based on the adjacency among the roof facades. Primitive extraction and facade intersections are used for building reconstruction. For overcome the difficulty just reconstruct of laser points data used with digital camera images. Also, 3D buildings of city area reconstructed using digital map. Finally, In this paper show 3D building Modeling using digital map and LiDAR data.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.286-290
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• 2009

In recent years, LiDAR technology has been becoming more popular and important. Its applications are completely replacing the traditional remote sensing technique. One of these applications is creating Digital City Models in urban areas, which is essential for many others such as disaster management, cartographic mapping, simulation of new buildings, updating and keeping cadastral data. In most of these cases the building outlines is the primary feature of interest. In this paper, a method of extracting building outlines from LiDAR data will be performed.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.637-641
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• 2007

Airborne LIDAR (Light Detection and Ranging) technology has reached a degree of the required accuracy in mapping professions, and advanced LIDAR systems are becoming increasingly common in the various fields of application. LiDAR data constitute an excellent source of information for reconstructing the Earth's surface due to capability of rapid and dense 3D spatial data acquisition with high accuracy. However, organizing the LIDAR data and extracting information from the data are difficult tasks because LIDAR data are composed of randomly distributed point clouds and do not provide sufficient semantic information. The main reason for this difficulty in processing LIDAR data is that the data provide only irregularly spaced point coordinates without topological and relational information among the points. This study introduces an efficient and robust method for automatic extraction of building footprints using airborne LIDAR data. The proposed method separates ground and non-ground data based on the histogram analysis and then rearranges the building boundary points using convex hull algorithm to extract building footprints. The method was implemented to LIDAR data of the heavily built-up area. Experimental results showed the feasibility and efficiency of the proposed method for automatic producing building layers of the large scale digital maps and 3D building reconstruction.

• Journal of the Regional Association of Architectural Institute of Korea
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• v.20 no.6
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• pp.81-87
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• 2018

In this paper, in order to maximize the input process efficiency of the building energy simulation field, the authors developed the automatic extraction module of spatial information based BIM geometry information. Existing research or software extracts geometry information based on object information, but it can not be used in the field of energy simulation because it is inconsistent with the geometry information of the object constituting the thermal zone of the actual building model. Especially, IFC-based geometry information extraction module is needed to link with other architectural fields from the viewpoint of reuse of building information. The study method is as follows. (1) Grasp the category and attribute information to be extracted for energy simulation and Analyze the IFC structure based on spatial information (2) Design the algorithm for extracting and reprocessing information for energy simulation from IFC file (use programming language Phython) (3) Develop the module that generates a geometry information database based on spatial information using reprocessed information (4) Verify the accuracy of the development module. In this paper, the reprocessed information can be directly used for energy simulation and it can be widely used regardless of the kind of energy simulation software because it is provided in database format. Therefore, it is expected that the energy simulation process efficiency in actual practice can be maximized.

• Journal of Broadcast Engineering
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• v.13 no.2
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• pp.236-244
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• 2008

We propose a new building detection and description algorithm for Lidar data and photogrammetric imagery using color segmentation, line segments matching, perceptual grouping. Our algorithm consists of two steps. In the first step, from the initial building regions extracted from Lidar data and the color segmentation results from the photogrammetric imagery, we extract coarse building boundaries based on the Lidar results with split and merge technique from aerial imagery. In the secondstep, we extract precise building boundaries based on coarse building boundaries and edges from aerial imagery using line segments matching and perceptual grouping. The contribution of this algorithm is that color information in photogrammetric imagery is used to complement collapsed building boundaries obtained by Lidar. Moreover, linearity of the edges and construction of closed roof form are used to reflect the characteristic of man-made object. Experimental results on multisensor data demonstrate that the proposed algorithm produces more accurate and reliable results than Lidar sensor.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.339-353
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• 2023

As the availability of high-resolution satellite imagery increases, improvement of positioning accuracy of satellite images is required. The importance of orthorectified images is also increasing, which removes relief displacement and establishes true localization of man-made structures. In this paper, we performed automated extraction of building rooftops and total building areas within original satellite images using the existing building height database. We relocated the rooftop sin their true position and generated an orthorectified building layer. The extracted total building areas were used to blank out building areas and generate true orthographic non-building layer. A final orthorectified image was provided by overlapping the building layer and non-building layer.We tested the proposed method with KOMPSAT-3 and KOMPSAT-3A satellite images and verified the results by overlapping with a digital topographical map. Test results showed that orthorectified building layers were generated with a position error of 0.4m.Through the proposed method, the feasibility of automated true orthoimage generation within dense urban areas was confirmed.

• Journal of Korean Society for Geospatial Information Science
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• v.7 no.1 s.13
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• pp.75-86
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• 1999

Three dimensional city model is composed of man-made and natural features, among these, most of man-made features are buildings. Therefore, it is very important to extract the building informations accurately and promptly to update the existing database. To achieve this, DTM can be reconstructed using building Information which is extracted from DTM, then this can be used as three dimensional city model. Thus, this paper aims to extract building boundaries and heights from high resolution DTM and edge informations of aerial photograph using mathematical morphology and image segmentation. We found that it is possible to extract buildings using opening operation in mathematical morphology and to improve the accuracy of building extraction using edge informations from aerial photograph.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.471-477
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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 ${\pm}$8.1cm, ${\pm}$24.7cm, ${\pm}$35.9cm, respectively.