• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.84-93
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• 2020

In recent years, the scale of damage from disasters such as earthquakes and large-scale fires and floods that are occurring in Korea is increasing. Accordingly, interest in urban disaster prevention that combines living infrastructure such as roads and parks is boosting, and it is urgent to prepare measures to reduce the damage scale of local cities. The purpose of this study is to derive implications for disaster prevention measures in areas where disaster prevention safety of local cities is weak through examples of disaster prevention safety of local cities in case of disaster. To this end, this study analyzed the regional characteristics, current status, and disaster prevention problems of regional cities in Japan, and selected disaster-vulnerable areas, and considered the distance relationship between disaster prevention bases through road network analysis. In addition, road closure simulation using ArcGIS Network Analyst was conducted to analyze disaster prevention safety in the area. As a result, the situation of the village which has a high possibility of isolation by natural disasters was grasped in advance. Through this, the suburbs confirmed the necessity of supplementing the disaster prevention function through transportation maintenance such as forest roads, and it was found that the city needs to prepare a risk management system. Furthermore, this study suggests the need for research on areas with a high possibility of isolation, especially in areas where disaster prevention functions are weak in local cities in case of disaster, and shows countermeasures for disaster prevention measures and resident education.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.2
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• pp.108-120
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• 2008

This paper discusses the prediction of deforestation areas using probability models from forest census database, Geographic information system (GIS) database and the land cover database. The land cover data was analyzed using remotely-sensed (RS) data of the Landsat TM data from 1989 to 2001. Over the analysis period of 12 years, the deforestation area was about 40ha. Most of the deforestation areas were attributable to road construction and residential development activities. About 80% of the deforestation areas for residential development were found within 100m of the road network. More than 20% of the deforestation areas for forest road construction were within 100m of the road network. Geographic factors and vegetation change detection (VCD) factors were used in probability models to construct deforestation occurrence map. We examined the size effect of area partition as training area and validation area for the probability models. The Bayes model provided a better deforestation prediction rate than that of the regression model.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.4 no.3
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• pp.75-83
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• 2001

This study selected Yangpyung as a target site because Yangpyung is a area of high value blessed with well preserved natural resources and beautiful natural scene and where thoughtless development and malformed use of land are in progress under the mask of hotel, accommodations and sales facility which fits the interests of land owner. As for the method used for this study, I inquired into domestic/international instances and the concept of existing environmental indicators and assessment of suitability with a view to establish assessment indicators and pose the concept through theoretical investigation of green-network and to establish green-network. 17 articles of environmental indicators such as aspect analysis, contour, greenbelt, DEM, NDVI, nature conservation area, reservoir and area for the promotion of agriculture were chosen as a actual analysing n data for setting up assessment indicators. From the result of analysis, as anticipated, green zone in Yongmusan areas within Youngmun and Seojong areas were the center of green-network in the wide area green-network in Yangpyung and the restricted area by basin system, road and legal regulation was selected as spot area and finally arable land and reservoir were selected as base which connects core with spot.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.94-106
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• 2022

In order to increase timber self-sufficiency, Korea's 6th Basic Forest Plan aims to increase the density of forest roads to 12.8 m ha^-1 by 2037. However, due to rapid re-forestation, current management infrastructure is insufficient, with just 4.8 m ha^-1 of forest roads in 2017. This is partly due to time and cost limitations on the process of forest road feasibility evaluation, which considers factors such as topography and forest conditions. To solve this problem, we propose an eco-friendly and efficient forest road network planning method using a geographic information system (GIS), which can evaluate a potential road site remotely based on spatial information. To facilitate such planning, this study identifies forest road construction priorities that can be evaluated using spatial information, such as topography, forest type and forest disasters. A method of predicting the optimal route to connect a forest road with existing roads is also derived. Overlapping analysis was performed using GIS-MCE (which combines GIS with multi-criteria evaluation), targeting the areas of Cheongsong-gun and Buk-gu, Pohang-si, which have a low forest-road density. Each factor affecting the suitability of a proposed new forest road site was assigned a cost, creating a cost surface that facilitates prioritization for each forest type. The forest path's optimal route was then derived using least-cost path analysis. The results of this process were 30 forestry site recommendations in Cheongsong-gun and one in Buk-gu, Pohang-si; this would increase forest road density for the managed forest sites in Cheongsong-gun from 1.58 m ha^-1 to 2.55 m ha^-1. This evaluation method can contribute to the policy of increasing timber self-sufficiency by providing clear guidelines for selecting forest road construction sites and predicting optimal connections to the existing road network.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.423-436
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• 2017

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.5
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• pp.83-90
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• 2010

The purpose of this study is to establish the standard of fire lane design to improve the performance of disaster prevention in local area. To accomplish it, we have focused on three research methods as below: first, we have expected disaster occurrence based on emergency tendency in local area; second, we have verified fire lane routes design through fire lane modeling and network analysis; third, we have quantitatively examined a possibility of danger of fire lanes through current road network modeling and analysis. The result of this study could be helpful to effective fire lane designs and quantitative analyses.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.6
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• pp.575-582
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• 2016

Seoul city has high population density as well as high traffic congestion, which are vulnerable to exposure of environmental pollutions caused by car traffic. However, recent studies are only on local regions about road traffic and air pollution or health effect of road traffic on residents. Thus, comprehensive study data are needed in terms of overall Seoul regions. In this study utilized the nitrogen dioxide concentration through the national air pollution monitoring network data, 2012 to 2013. It also divided regions into high and low exposure districts via the Origin destination data developed by the Korea transport institute to quantify and evaluate the effect of transport policies and analyzed a correlation of asthma symptoms with high and low exposure districts through raw data of community health survey from the Korea centers for disease control and prevention. Based on the collected data, the pearson's correlation analysis was conducted between air pollution substance concentration and high exposure district and multiple logistic regression analysis was conducted to determine the effect of traffic environment and factors on asthma symptoms of residents. Accordingly, the following results were derived. First, the high exposure district was higher concentrations of nitrogen dioxide ($NO_2$) as per time compared to those of the low exposure district (p<0.01). Second, analysis on correlation between average daily environmental concentration in the air pollution monitoring network and road traffic showed that nitrogen dioxide had a significant positive correlation (p<0.01) with car traffic and total traffic as well as with truck traffic (p<0.05) statistically. Third, an adjusted odds ratio about asthma doctor's diagnosis in the high and low exposure districts was analyzed through the logistic regression analysis. With regard to an adjusted model 2 (adjusted gender, age, health behavior characteristics, and demographic characteristics) odds ratio of asthma doctor's diagnosis in the high exposure district was 1.624 (95% CI: 1.269~2.077) compared to that of the low exposure district, which was significant statistically (p<0.001).

• International Journal of Highway Engineering
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• v.2 no.3
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• pp.145-153
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• 2000

Pavement evaluation is a fundamental component for rational pavement management. Optimal rehabilitation method and the priority of rehabilitation should be based on the evaluation data. Some types of pavement condition index are needed for objective evaluation of Pavement condition and management of road network. In this study a expressway concrete pavement condition index model is developed through regression analysis that correlates panel rating with distress measurement from the test sections. The derived condition index can be used for network level PMS for the expressway concrete pavement. Correlation coefficient of the model was 0.68. The selected independent variables were International Roughness Index, crack and area of patching.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.4
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• pp.415-421
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• 2008

The purpose of this study is to provide a sensor based service model for traffic facilities in u-City through analysing the present condition of urban infrastructure, especially traffic facilities management. In order to achieve this purpose, we did a comparative analysis of internal and external situation of ITS(Intelligent Transport System) and classified service for traffic facilities to 3 categories, namely traffic flow management, traffic information offering, formation of road structure. Also, we examined technological trend of USN(Ubiquitous Sensor Network). Through this process, we present a sensor based service model of traffic facilities for building sustainable road environment.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.223-224
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• 2021

Recently, k-farthest neighbor (kFN) queries have not as much attention as k-nearest neighbor (kNN) queries. Therefore, this study considers moving k-farthest neighbor (MkFN) queries for spatial network databases. Given a positive integer k, a moving query point q, and a set of data points P, MkFN queries can constantly retrieve k data points that are farthest from the query point q. The challenge with processing MkFN queries in spatial networks is to avoid unnecessary or superfluous distance calculations between the query and associated data points. This study proposes a batch processing algorithm, called MOFA, to enable efficient processing of MkFN queries in spatial networks. MOFA aims to avoid dispensable distance computations based on the clustering of both query and data points. Moreover, a time complexity analysis is presented to clarify the effect of the clustering method on the query processing time. Extensive experiments using real-world roadmaps demonstrated the efficiency and scalability of the MOFA when compared with a conventional solution.