• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.04a
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• pp.89-96
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• 2000

Landslide damage comprise most part of the damages from the earthquake and it only causes the damage to lives and structures directly but also cease the operation of social system by road or lifeline failure. For these reasons hazard assesment on the landslides has been recognized very important. And hazard maps have been used to visualize the hazard of the landslide. In this study as first step for application of hazard map to domestic cases hazard maps are made for the Ul-Joo Ul-san Korea, Where the Yan-san faults are located. For building hazard maps the degree of hazard are evaluated based on Newmark displacement and the resulting maps are constructed by GIS technique. In hazard assesment maximum ground acceleration obtained from attenuation equation of wave propagation and design earthquake acceleration suggested by Ministry of construction are used for acceleration term. Hazard maps are made by GIS programs Arc/Info and Arc/View based on the digital maps and data from lab tests and elastic wave surveys The maps show the possible landslide regions significantly and the displacements of slide are proportional to the slope angles.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.238-242
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• 2007

Today's commercial high resolution satellite imagery such as that provided by IKONOS and QuickBird, offers the potential to extract useful spatial information for geographical database construction and GIS applications. Digital maps supply the most generally used GIS data probiding topography, road, and building information. Currently, the building information provided by digital maps is incompletely constructed for GIS applications due to planar position error and warped shape. We focus on extracting of the accurate building information including position, shape, and height to update the building information of the digital maps and GIS database. In this paper, we propose a new method of 3D building information extraction with a single high resolution satellite image and digital map. Co-registration between the QuickBird image and the 1:1,000 digital maps was carried out automatically using the RPC adjustment model and the building layer of the digital map was projected onto the image. The building roof boundaries were detected using the building layer from the digital map based on the satellite azimuth. The building shape could be modified using a snake algorithm. Then we measured the building height and traced the building bottom automatically using triangular vector structure (TVS) hypothesis. In order to evaluate the proposed method, we estimated accuracy of the extracted building information using LiDAR DSM.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.1
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• pp.45-52
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• 2015

The interoperability of multi-source data has become more important due to various digital maps, produced from public institutions and enterprises. In this study, the automatic matching algorithm of multi-source building data using hierarchical matching was proposed. At first, we divide digital maps into blocks and perform the primary geometric registration of buildings with the ICP algorithm. Then, corresponding building pairs were determined by evaluating the similarity of overlap area, and the matching threshold value of similarity was automatically derived by the Otsu binary thresholding. After the first matching, we extracted error matching candidates buildings which are similar with threshold value to conduct the secondary ICP matching and to make a matching decision using turning angle function analysis. For the evaluation, the proposed method was applied to representative public digital maps, road name address map and digital topographic map 2.0. As a result, the F measures of matching and non-matching buildings increased by 2% and 17%, respectively. Therefore, the proposed method is efficient for the matching of building polygons from multi-source digital maps.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.11
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• pp.123-132
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• 1998

This paper presents a road detection technique for spaceborne synthetic aperture radar (SAR) images. Roads are important cartographic features. We incorporate an active contour model called snake as a model for the road and define a new external energy for snake which is appropriate for the road. Detecting roads in spaceborne SAR images is very difficult without other information. In this paper, digital maps are utilized to obtain the initial position and shape for snake. Only approximate geodetic location of roads appearing in SAR images can be known through geocoding process and usual digital maps also have location errors. Therefore, there exist large location offsets between the two data. By introducing initial matching procedure, the errors are reduced significantly. Then we initialize the snake's shape using the roads extracted from digital map and minimize the energies of all snake points to detect roads. We outline two problems in detection and propose a method that mitigates them.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.3-10
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• 2014

Digital maps are made by experts who digitize the data from aerial image and field survey. And the digital maps are updated every 2 years in National Geographic Information Institute. Conventional Digitizing methods take a lot of time and cost. And geographic information needs to be modified and updated appropriately as geographical features are changing rapidly. Therefore in this paper, we modify the digital map updates the road information for rapid high-resolution aerial orthophoto taken at different times were performed HSI color conversion. Road area of the cassification was performed the region growing methods. In addition, changes in the target area for analysis by applying the CVA technique to compare the changed road area by analyzing the accuracy of the proposed extraction.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.2
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• pp.156-162
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• 2012

This paper presents differences of road traffic noise maps were generated by using the draft map and two digital maps with different versions. As a first step, the calculation of the areas of excessive noise exposure was made for the draft map and each digital map version. Subsequently, the areas of excessive noise exposure were compared so as to determine how different from each other. Then, comparison of the populations exposed to excessive noise was also conducted in the same way. It was found that the most accurate noise map was obtained when using the combination of the draft map containing all attribute information and the digital map Ver 2.0. This result indicates that more information on the height and the number of floors of the individual building is required in order to obtain more accurate population exposed to excessive noise, thus creating a more accurate noise map.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.3
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• pp.229-238
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• 2013

Development of methodologies to generate the small scale map from the large scale map using map generalization has huge importance in management of the digital topographic map, such as producing and updating maps. In this study, the selection methodology of map generalization for the road network data in digital topographic map is investigated and evaluated. The existing maps with 1:5,000 and 1:25,000 scales are compared and the criteria for selection of the road network data, which are the number of objects and the relative importance of road network, are analyzed by using the T$\ddot{o}$pfer's radical law and Logit model. The selection model derived from the analysis result is applied to the test data, and the road network data of 1:18,000 and 1:72,000 scales from the digital topographic map of 1:5,000 scale are generated. The generalized results showed that the road objects with relatively high importance are selected appropriately according to the target scale levels after the qualitative and quantitative evaluations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.1
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• pp.21-28
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• 2011

In the existing digital map of the Ver.1.0, it is impossible to make a small scale digital map, which is under the 1/5000 scale map, by using the 1/1000 digital map which is the most large scale one. Because of this reason, the existing digital maps are produced into a 1/1000 and a 1/5000 map by means of two different scale aerial photos. The next generation digital map should be successively related to a small scale digital map based on the most large scale digital one. This is so important from the aspects of data share and the consecutive renewal. Ever since the development of the digital map of the Ver. 2.0, the possibility of making a multi-scale consecutive digital map has been presented and the related research has been done again. The most basic thing in the multi-scale digital maps is to decide the criteria of the generalization between the two scales. In this study, I try to formulate the criteria of the generalization required to make the 1/5000 digital map by using the 111000 digital one. In addition, I by to explore the application possibility of the consecutive renewal by carrying out auto-generalization.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.1
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• pp.47-53
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• 2011

To enable the efficient operation of ITS, it is necessary to collect location data for vehicles on the road. In the case of futuristic transportation systems like ubiquitous transportation and smart highway, a method of data collection that is advanced enough to incorporate road lane recognition is required. To meet this requirement, technology based on radio frequency identification (RFID) has been researched. However, RFID may fail to yield accurate location information during high-speed driving because of the time required for communication between the tag and the reader. Moreover, installing tags across all roads necessarily incurs an enormous cost. One cost-saving alternative currently being researched is to utilize GNSS (global navigation satellite system) carrierbased location information where available. For lane recognition using GNSS, a precise digital map for determining vehicle position by lane is needed in addition to the carrier-based GNSS location data. A "precise digital map" is a map containing the location information of each road lane to enable lane recognition. At present, precise digital maps are being created for lane recognition experiments by measuring the lanes in the test area. However, such work is being carried out through comparison with vehicle driving information, without definitions being established for detailed performance specifications. Therefore, this study analyzes the performance requirements of a precise digital map capable of lane recognition based on the accuracy of GNSS location information and the accuracy of the precise digital map. To analyze the performance of the precise digital map, simulations are carried out. The results show that to have high performance of this system, we need under 0.5m accuracy of the precise digital map.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.289-297
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• 2009

In the paper, we propose the methodology to extract training dataset automatically for supervised classification of road networks. For the preprocessing, we co-register the airborne photos, LIDAR data and large-scale digital maps and then, create orthophotos and intensity images. By overlaying the large-scale digital maps onto generated images, we can extract the initial training dataset for the supervised classification of road networks. However, the initial training information is distorted because there are errors propagated from registration process and, also, there are generally various objects in the road networks such as asphalt, road marks, vegetation, cars and so on. As such, to generate the training information only for the road surface, we apply the Expectation Maximization technique and finally, extract the training dataset of the road surface. For the accuracy test, we compare the training dataset with manually extracted ones. Through the statistical tests, we can identify that the developed method is valid.