• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.4
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• pp.112-123
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• 2015

The purpose of this study is to analyze the biotope characteristics of Korean cities and set up biotope type structures for Korean cities based on biotope type classification, dominant biotope type, city's human and nature environmental characteristics and cluster analysis. The findings of the study are summarized as follows: First, regarding the analysis of biotope type classification, cities showed differences in terms of the standard of biotope classification and classification hierarchy. Next, the analysis of dominant biotope types showed the type of forest represents the largest area in most cities. Moreover, a city's characteristic analysis revealed large differences between cities. As a result of cluster analysis, cities were classified into five clusters overall. First, Cluster A showed a lower population level and urbanization level. Unlike other cities, Cluster A revealed that it has the largest percentage of agricultural areas. Cluster C showed very high levels in terms of population amount and urbanization conditions was named the 'Large-sized metropolitan cities-center of forest biotope area' based on it's characteristics. The findings of this study as summarized above are considered to play an important role in enabling detailed classification and preservation of biotope types fit for the characteristics of cities and minimizing the confusion caused by different biotope mapping methods when revising and complementing biotope maps.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.171-183
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• 2018

Despite the rapid expansion of satellite images supply, the application of imagery is often restricted due to unautomated image processing. This paper presents the automated process for the selection of training areas which are essential to conducting supervised image classification. The training areas were selected based on the prior and cover information. After the selection, the training data were used to classify land cover in an urban area with the latest image and the classification accuracy was valuated. The automatic selection of training area was processed with following steps, 1) to redraw inner areas of prior land cover polygon with negative buffer (-15m) 2) to select the polygons with proper size of area ($2,000{\sim}200,000m^2$) 3) to calculate the mean and standard deviation of reflectance and NDVI of the polygons 4) to select the polygons having characteristic mean value of each land cover type with minimum standard deviation. The supervised image classification was conducted using the automatically selected training data with Sentinel-2 images in 2017. The accuracy of land cover classification was 86.9% ($\hat{K}=0.81$). The result shows that the process of automatic selection is effective in image processing and able to contribute to solving the bottleneck in the application of imagery.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.4
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• pp.37-52
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• 2022

The purpose of this study is to analyze the demand for cultural ecosystem services in urban forests based on user perception and experience value by using Naver blog posts and LDA topic modeling. Bukhansan National Park was used to analyze and review the feasibility of spatial assessments. Based on the results of topic modeling from blog posts, a review process was conducted considering the relevance of Bukhansan National Park's cultural services and its suitability as a spatial assessment case, and finally, an index for the spatial assessment of urban forest's cultural service was derived. Specifically, 21 topics derived through topic analysis were interpreted, and 13 topics related to cultural ecosystem services were derived based on the MA(Millennium Ecosystem Assessment)'s classification system for ecosystem services. 72.7% of all documents reviewed had data deemed useful for this study. The contents of the topic fell into one of the seven types of cultural services related to "mountainous recreation activities" (23.7%), "indirect use value linked to tourism and convenience facilities" (12.4%), "inspirational activities" (11.2%), "seasonal recreation activities" (6.2%), "natural appreciation and static recreation activities" (3.7%). Next, for the 13 cultural service topics derived from data gathered about Bukhansan National Park, the possibility of spatial assessment of the characteristics of cultural ecosystem services provided by urban forests was reviewed, and a total of 8 cultural service indicators were derived. The MA's cultural service classification system for ecosystem services, which was widely used in previous studies, has limitations in that it does not reflect the actual user demand of urban forests, but it is meaningful in that it categorizes cultural service indicators suitable for domestic circumstances. In addition, the study is significant as it presented a methodology to interpret and derive the demand for cultural services using a large amount of user awareness and experience data.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.1
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• pp.89-100
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• 2010

The aim of this study is to produce land cover maps using satellite imagery with various degrees of high resolution and then compare the accuracy of the image types and categories. For the land cover map produced on a small-scale classification the estuary area around the Nakdong river, including an urban area, farming land and waters, was selected. The images were classified by analyzing the aerial photos taken from KOMPSAT2, Quickbird and IKONOS satellites, which all have a resolution of over 1m to the naked eye. Once all of the land cover maps with different images and land cover categories had been produced they were compared to each other. Results show that image accuracy from the aerial photos and Quickbird was relatively higher than with KOMPSAT2 and IKONOS. The agreement ratio for the large-scale classification across the classification methods ranged between 0.934 and 0.956 for most cases. The Kappa value ranged between 0.905 and 0.937; the agreement ratio for the middle-scale classification was 0.888~0.913 and the Kappa value was 0.872~0.901. The agreement ratio for the small-scale classification was 0.833~0.901 and the Kappa value was 0.813~0.888. In addition, in terms of the degree of confusion occurrence across the images, there was confusion on the urbanized arid areas and empty land in the large-scale classification. For the middle-scale classification, the confusion mainly occurred on the rice paddies, fields, house cultivating area and artificial grassland. For the small-scale classification, confusion mainly occurred on natural green fields, cultivating land with facilities, tideland and the surface of the sea. The findings of this study indicate that the classification of the high resolution images with the naked eye showed an agreement ratio of over 80%, which means that it can be used in practice. The findings also suggest that the use of higher resolution images can lead to increased accuracy in classification, indicating that the time when the images are taken is important in producing land cover maps.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.09a
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• pp.277-282
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• 2010

IT를 매개로 서로 다른 분야의 기술들이 상호 융합되어 새로운 서비스 또는 기술을 제공하는 유비쿼터스 패러다임은 도시에 대한 새로운 시각을 제공하고 있다. u-City는 첨단 정보통신 인프라와 유비쿼터스 정보서비스를 도시공간에 융합하여 도시생활의 편의 증대와 삶의 질 향상, 체계적 도시관리에 의한 안전보장과 시민복지 향상, 신산업 창출 등 도시의 제반기능을 혁신시킬 수 있는 차세대 정보화 도시를 의미한다. 성공적인 u-City 건설을 위해서 기존의 지자체GIS 개념을 확대한 지능형 도시공간정보의 개념이 필요하며, 지능형 도시공간정보를 효율적으로 제공하기 위한 서비스가 요구된다. 이러한 지능형 도시공간정보 서비스는 도시 전반의 영역에서 제시될 수 있으므로 다양한 서비스를 도출할 수 있다. 이에 본 연구에서는 지능형 도시공간정보의 개념을 살펴보고, 기존 u-City 관련 서비스 분류에 관한 선행 연구내용을 중심으로 지능형 도시공간정보 서비스 분류체계를 제시였다.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.115-120
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• 2015

Recently, in automotive technology area, intelligent safety systems have been actively accomplished for drivers, passengers, and pedestrians. Also, many researches are focused on development of autonomous vehicles. This paper propose the application of LiDAR sensors, which takes major role in perceiving environment, terrain classification, obstacle data clustering method, and local map building for autonomous driving. Finally, based on these results, planning for lane change path that vehicle tracking possible were created and the reliability of path generation were experimented.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.321-339
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• 2017

In this work, we are interested in the extraction of areas of interest from satellite images by introducing a MO-TRIBES/OC-SVM approach. The One-Class Support Vector Machine (OC-SVM) is based on the estimation of a support that includes training data. It identifies areas of interest without including other classes from the scene. We propose generating optimal training data using the Multi-Objective TRIBES (MO-TRIBES) to improve the performances of the OC-SVM. The MO-TRIBES is a parameter-free optimization technique that manages the search space in tribes composed of agents. It makes different behavioral and structural adaptations to minimize the false positive and false negative rates of the OC-SVM. We have applied our proposed approach for the extraction of earthquakes and urban areas. The experimental results and comparisons with different state-of-the-art classifiers confirm the efficiency and the robustness of the proposed approach.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.179-185
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• 2018

Particulate Matter (PM2.5) has various adverse effects on health. Climate and industry activity and traffic volume are the main causes, especially in urban area. In order to construct an effective forecasting system, many measurement systems are required, but it is impossible in reality. Therefore, in this study, we propose a method to infer PM2.5 condition by using rule induction technique. The experimental results showed a classification accuracy of 71%.

• Spatial Information Research
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• v.6 no.1
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• pp.35-45
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• 1998

In order to support effective decision-makings related to greenery planning, this study construct a fundamental database about greenery information for Seoul metropolitan area using GIS and Remote Sensing. Greenery evaluation index consists of ecological/ environmental, utilization, social condition value which are used to determine the development priority of undevelopment parks. These indexes will also be utilized for the green area conservation classification for Seoul metropolitan area. This study is a part of previous project called "Management Policy of the Total Amout of Greenery", which was conducted in Seoul Development Institute.Institute.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.537-543
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• 2003

도시의 물리적 성장을 분석하기Hl 원격탐사 자료는 매우 유용한 도구를 제공한다 할 수 있다. 도시의 물리적 성장은 도시의 토지이용과 밀접하게 관련되어 있으며 지속 가능한 도시성장을 위하여서는 토지이용을 중심으로 한 성장관리가 필수적이다. 그러나 위성영상을 이용한 도시 토지이용의 분류는 우선 그 기준이 사용자의 관점에 따라 다르고 영상의 해상도 등에 따라 달리 그 기준이 정해질 수 있다. 도시의 성장 분석을 위해서는 다중시기의 위성영상 및 항공사진을 이용하여 토지이용 분류를 수행하고 시기별 토지이용 변화와 양상을 분석함으로써 성장요인을 추출하고 이를 기반으로 향후의 도시 성장을 예측할 수 있는 성장모델 개발이 가능해 진다. 따라서 본 연구에서는 도시성장 예측모델 개발의 전 단계로써 도시의 성장관리를 위해 사용되는 다양한 공간 해상도를 지닌 원격탐사 자료의 국내외 다양한 분류기준의 검토를 통해 토지이용 분류 기준을 도시 성장관리의 측면에서 설정하고자 한다.