• Journal of the Korean Institute of Rural Architecture
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• v.17 no.1
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• pp.45-54
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• 2015

This study is about the Type classification of architectural assets in Seosungro (one of the 4 Roads of Rampart in Junggu, Daegu), the main stronghold of Urban Regeneration projects according to the recent Urban Regeneration law. The purpose of this study is to suggest the basic data about Operation management method of Original Downtown modern buildings and valuable hanok, and Conservational Regeneration of Architectural property of Urban Environmental Improvement and Architectural assets. By researching, analysing the feature and classifying the type of the buildings in Seosungro, The type classified Conservation plan can be suggested. The Types of the Architectural assets will be the basic data of the application plan of modern buildings which is for the urban regeneration, and this can predict the quantity and the demand of the building for effective urban regeneration, and also can be an effective Urban regeneration policy data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.32-38
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• 2018

Studies on classification of national roads have been continued, but there is little research on the classification of urban arterial roads. Due to the increase of traffic volume, urban arterial roads do not perform well as main roads. In this paper, the function of urban arterial road was established by using cluster analysis using traffic characteristics. Traffic characteristics such as traffic volume, weekend coefficient and speed coefficient were used to establish the functions of 55 main arterial roads in Seoul. The results of this paper are compared with those of the method using AADT. The method using AADT classifies the characteristics according to the traffic volume of the whole lane. In this paper, however, the results are derived using the traffic volume per lane reflecting the actual traffic volume. In addition, the functional classification of the arterial roads in Seoul was compared with the results of this paper to verify that the traffic characteristics were reflected. As a result, the method presented in this paper is more effective in showing traffic characteristics than the current highway functional classification method, and the functional classification system will be helpful for road extension and planning design.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1567-1577
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• 2020

Urban land cover classification is role in urban planning and management. So, it's important to improve classification accuracy on urban location. In this paper, machine learning model, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are proposed for urban land cover classification based on high resolution satellite imagery (KOMPSAT-3A). Satellite image was trained based on 25 m rectangle grid to create training data, and training models used for classifying test area. During the validation process, we presented confusion matrix for each result with 250 Ground Truth Points (GTP). Of the four SVM kernels and the two activation functions ANN, the SVM Polynomial kernel model had the highest accuracy of 86%. In the process of comparing the SVM and ANN using GTP, the SVM model was more effective than the ANN model for KOMPSAT-3A classification. Among the four classes (building, road, vegetation, and bare-soil), building class showed the lowest classification accuracy due to the shadow caused by the high rise building.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.984-987
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• 2003

In order to rapidly catch up urban region’s detailed land-use or land-cover information; this research used the post-classification algorithm (Spatial Reclassification Kernel: SPARK) to create a land-use map of Taichung City. We discussed the urban land-use classification model with the IKONOS images. The conclusions may be distinguished as follows:(a) Using the Maximum-Likelihood algorithm to classify seven broad land-cover categories. The overall accuracy in this stage achieves 92.72% and Kappa coefficient will be obtained 0.91; and (b) Using the SPARK method to classify images for detect the land-use, the overall accuracy achieves higher 89.64% and Kappa coefficient will be 0.86. To conclude, the research process in this study can fully and carefully describe local land-use pattern and assist the demand of land management and resources planning reference.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.10-13
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• 2004

Hyperspectral data has a great advantage to classify various surface materials that are spectrally similar. In this study, we attempted to classify man-made materials in urban area using Hyperion data. Hyperion imagery of Seoul was initially processed to minimize radiometric distortions caused by sensor and atmosphere. Using color aerial photographs. we defined seven man-made surfaces (concrete, asphalt road. railroad, buildings, roof, soil, shadow) for the classification in Seoul. The hyperspectral data showed the potential to identify those manmade materials that were difficult to be classified by multispectral data. However. the classification of road and buildings was not quite satisfactory due to the relatively low spatial resolution of Hyperion image. Further, the low radiometric quality of Hyperion sensor was another limitation for the application in urban area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.4
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• pp.385-393
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• 2001

Urban land use information is the base data for the urban planning, district-level planning, traffic impact assessment and environmental impact assessment, etc. The level of detail of the current land use information is not enough to analysis and planning. In this study, the status and problems of the current land use information is analysed. The advanced abroad cases, such as LBCS(Land Based Classification System) of American Planning Association, are studied. The purpose of this study is to develop the coding system for urban land use information classification. Through this system, it is anticipated to standardization of land use classification system and improvement of data compactability.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• Journal of the Korean Institute of Landscape Architecture
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• v.33 no.4 s.111
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• pp.1-10
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• 2005

Recently, as urban landscape is growing in importance, urban landscape planning is being actively performed. for this purpose, classification of the urban landscape is definitely required. Therefore, this research focuses on classifying urban landscape in Daejeon metropolis by dividing the urban land use pattern. This results are as follows. 1. Urban land use pattern is divided into 20 classes. The residential, commercial and industrial areas, the old market and the bus terminal are evaluated negatively, whereas the areas of school, water reservoir, neighborhood park and train station are appreciated as being positive in landscape characters. 2. As a result of a cluster analysis, urban landscape has five different landscape types. These are: landscapes of medium diversity lacking green area, landscapes of high diversity lacking green area, landscapes rich in green area and with medium diversity, landscapes rich in green area and with high diversity, and landscapes rich in green area and with low diversity. 3. In landscape characters of beauty and harmony, landscapes rich in green area and with medium diversity are more positively evaluated than those rich in green area and with low diversity. This point should be taken into account for planning the urban landscape.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.4
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• pp.81-86
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• 2009

This research is a part of the Intelligent Urban Facility Management project of the Korean Land Spatialization Group (KGSL). First, this study started from the investigation of existing u-City service model in order to drive essential components and considerations for the urban facilities management system. Considering the driven conclusions, this study finally proposed the new classification system of urban facilities management service model and the adequate application method in u-City.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.2
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• pp.23-29
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• 2008

This study deals with the systematic classification and analysis of urban facilities as a fundamental foundation of the intelligent and active management of urban facilities using Ubiquitous Sensor Network(USN). In the results of recent studies, the necessities and simple examples of USN application were already shown, but there were few studies about the systematic classification and specific application methods considering characteristics of urban facilities. In this study, we try to classify the main management characteristics of facilities and analyze the results, furthermore to contribute to the development of convergence of facility management and USN technology.