• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.3
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• pp.152-163
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• 2009

This study identified characteristics of households in Seo Geumsa based on factors related to how well each household understood and agreed or disagreed with the Newtown project and the resettlement process that is required to establish the renewal promotion plan. To this end, the authors applied the unit of spatial analysis developed by Tong, segmented the land intended for large-scale development, and then developed a method for analyzing and comparing the segmented lands by certain characteristics. The results of the survey were analyzed in three stages: the characteristics of districts; the relationships between agreement and disagreement factors and differences among segmented districts. And, to assess districts with features that differed from the overall features of households in renewal districts, the authors developed a two-way stage division plan and conducted a cluster analysis. The authors analyzed districts with individual characteristics based on the household features developed by Tong, and then analyzed the features of household distribution in these districts along with spatial location.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

• Journal of Korean Society of Rural Planning
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• v.20 no.3
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• pp.121-130
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• 2014

This study aimed at setting up the evaluation indicators system by rural village types to identify systematically the multi-valuedness embedded in rural villages. AHP(Analytic Hierarchy Process) was used for evaluating the relative importance weight evaluation of each indicator and quantitative analysis of rural village through computer works. The importance weight of evaluation indicators was converted into the score on the basis of maximum 1,000 point to increase the practicality. As a result, characteristics of 5 rural village types(Basic life-supporting, Agricultural promotion, Marketing/processing oriented, Urban-rural communication, Life-style choice types) differed in score of classified indicators. Also, These results are expected to be possible to quantitatively evaluate characteristics by rural village types.

• Proceedings of the KSRS Conference
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• v.1
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• pp.260-262
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• 2006

One of future remote sensing techniques for transportation application is vehicle detection from the space, which could be the basis of measuring traffic volume and recognizing traffic condition in the future. This paper introduces an approach to vehicle detection using image object segmentation approach. The object-oriented image processing is particularly beneficial to high-resolution image classification of urban area, which suffers from noisy components in general. The project site was Dae-Jeon metropolitan area and a set of true color aerial images at 10cm resolution was used for the test. Authors investigated a variety of parameters such as scale, color, and shape and produced a customized solution for vehicle detection, which is based on a knowledge-based hierarchical model in the environment of eCognition. The highest tumbling block of the vehicle detection in the given data sets was to discriminate vehicles in dark color from new black asphalt pavement. Except for the cases, the overall accuracy was over 90%.

• Korean Institute of Interior Design Journal
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• v.18 no.6
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• pp.158-165
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• 2009

Ever since the industrial revolution, large cities have become a field of new lifestyle and urbanization, causing climate change and environmental pollution. As a result, countermeasures for revolving these problems is needed. In addition, large cities in the information age have become a space where each nation executes its public policy to express the competitiveness of each city. In this study, countermeasures for the environmental crises caused by urbanization as well as the sustainable spatial designs for the cities are investigated as a new source of urban competitiveness, and the environmental aesthetics for designing public space is considered. The purpose of this study is to suggest a direction for sustainable designs and planning that is applicable to public space. According to the definition of sustainability, the items of the spatial implementation of ecological, economical and social sustainability are categorized. Based on this categorization, the sustainable designs of public space are classified into five types, and a comprehensive analysis of good public spaces from previous literature is conducted. The concepts of design and three elements--public space, sustainability, and their instrumental meanings, are integrated in this study. The significance of this study lies in the actual application of the classification to the planning and design of sustainable public space in cities, rather than being a conceptual classification.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.1
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• pp.12-29
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• 2016

The purpose of this study was to improve forestland characteristic evaluation system's indicators for rational development and ecosystem conservation. There has been no consideration for statistical duplication between variables, and it caused inefficient data collection. Furthermore, the same evaluation criteria were applied for all forestlands without considering regionally different characteristics, and it made variation for designation cancel rates of preservation semi-preservation forestlands between cities. To solve these problems, we first removed 'DBH' variable which has a multicollinearity. Second, we applied standard normal distribution for each forest watershed type. As a result of eliminating 'DBH', the numbers of parcels for all grades except A were changed but their numbers and areas were not large enough to consider the change of total score. For the output of analyses with the existing same regional criteria, the total scores of urban type and urban-fringe type forestlands were higher than those of other types. The numbers of parcels for A and B were increased and those for C and E were decreased by applying standard normal distribution. This caused the increase of preservation-oriented parcels. Finally, we suggested a new evaluation method based on standard normal distribution to consider regional forest characteristics and to solve regional imbalance.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.3
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• pp.77-86
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• 2002

Recently software industry in GIS(geographic information system) becomes an interesting issue by performing a large scale of national GIS application development as well as even small unit of FMS(facility management system). Also, there exist many cases to combine GIS with various business domains such as MIS(marketing information system), CNS(car navigation system) and ITS(intelligent transportation system). In this situation, in order to develop an efficient and useful GIS application for a short term, there must be a deep consideration of not only developing GIS component but also managing GIS component. In fact, even though there exist many certain components having high reusability, excellent interoperability and good quality, their reusability may be reduced because of their difficulty to access in a certain repository. Therefore, it is important to classify components having common characteristic based on their particular rule with reflecting their functionality and non-functionality before cataloging them. Here, there are two non-functional classification categories discussed such as GIS content-dependent metadata and GIS content-independent metadata. This cataloged components will help application developers to select easily their desired components. Moreover, new components may be easily producted by modifying and combining previous components. Finally, the original goal of all this effort can be defined through obtaining high reusability and interoperability of GIS component.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1233-1242
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• 2021

In this study, a model was developed to determine whether flooding occurred using image data, which is unstructured data. CNN-based VGG16 and VGG19 were used to develop the flood classification model. In order to develop a model, images of flooded and non-flooded images were collected using web crawling method. Since the data collected using the web crawling method contains noise data, data irrelevant to this study was primarily deleted, and secondly, the image size was changed to 224×224 for model application. In addition, image augmentation was performed by changing the angle of the image for diversity of image. Finally, learning was performed using 2,500 images of flooding and 2,500 images of non-flooding. As a result of model evaluation, the average classification performance of the model was found to be 97%. In the future, if the model developed through the results of this study is mounted on the CCTV control center system, it is judged that the respons against flood damage can be done quickly.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.759-766
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• 2008

Road classification system is the first step for determining the road function and design standards. Currently, roads are classified by various indices such as road location and function. In this study, we classify road using various traffic indices as well as to identify traffic characteristics for each type of road. To accomplish the objectives, mixture model was applied for classifying road and analyzing traffic characteristics using traffic data that observed at permanent traffic count stations. A total of 8 variables were applied: annual average daily traffic(AADT), $K_{30}$ coefficient, heavy vehicle proportion, day volume proportion, peak hour volume proportion, sunday coefficient, vacation coefficient, and coefficient of variation(COV). A total of 350 permanent traffic count points were categorized into three groups : Group I (Urban road), Group II (Rural road), and Group III (Recreational road). AADT were 30,000 for urban, 16,000 for rural, and 5,000 for recreational road. Group III was typical recreational road showing higher average daily traffic volume during Sunday and vacational periods. Group I showed AM peak and PM peak, while group II and group III did not show AM peak and PM peak.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.885-894
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• 2006

The purpose of this study is focused on the development of the effective classification technique using ultra multiband of hyperspectral image. This study suggests the classification technique using canonical correlation analysis, one of multivariate statistical analysis in hyperspectral image classification. High accuracy of classification result is expected for this classification technique as the number of bands increase. This technique is compared with Maximum Likelihood Classification(MLC). The hyperspectral image is the EO1-hyperion image acquired on September 2, 2001, and the number of bands for the experiment were chosen at 30, considering the band scope except the thermal band of Landsat TM. We chose the comparing base map as Ground Truth Data. We evaluate the accuracy by comparing this base map with the classification result image and performing overlay analysis visually. The result showed us that in MLC's case, it can't classify except water, and in case of water, it only classifies big lakes. But Canonical Correlation Classification (CCC) classifies the golf lawn exactly, and it classifies the highway line in the urban area well. In case of water, the ponds that are in golf ground area, the ponds in university, and pools are also classified well. As a result, although the training areas are selected without any trial and error, it was possible to get the exact classification result. Also, the ability to distinguish golf lawn from other vegetations in classification classes, and the ability to classify water was better than MLC technique. Conclusively, this CCC technique for hyperspectral image will be very useful for estimating harvest and detecting surface water. In advance, it will do an important role in the construction of GIS database using the spectral high resolution image, hyperspectral data.