• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.62-65
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• 2003

We propose an algorithm for almost unsupervised detection of linear structures, in particular, axes in road network and river, as seen in synthetics aperture radar (SAR) images. The first is local step and used to extract linear features from the speckle radar image, which are treated as road segment candidates. We present two local line detectors as well as a method for fusing information from these detectors. The second is global step, we identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.121-124
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• 2000

We propose an algorithm for almost unsupervised detection of linear structures, in particular, axes in road network and river, as seen in synthetics aperture radar (SAR) images. The first is local step and used to extract linear features from the speckle radar image, which are treated as road segment candidates. We present two local line detectors as well as a method for fusing Information from these detectors. The second is hybrid step, we Identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.221-224
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• 2000

We propose an algorithm for almost unsupervised detection of linear structures, in particular, axes in road network and river, as seen in synthetics aperture radar (SAR) images. The first is local step and used to extract linear features from the speckle radar image, which are treated as road segment candidates. We present two local line detectors as well as a method for fusing information from these detectors. The second is global step, we identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects.

• Journal of Korean Society of Forest Science
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• v.81 no.4
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• pp.363-371
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• 1992

This study was aimed at developing a computer model to determine rational road networks in mountainous forests. The computer model is composed of two major subroutines for digital terrain analyses and route selection. The digital terrain model(DTM) provides various information on topographic and vegetative characteristics of forest stands. The DTM also evaluates the effectiveness of road construction based on slope gradients. Using the results of digital terrain analyses, the route selection subroutine, heuristically, determines the optimal road layout satisfying the predefined road densities. The route selection subroutine uses the area-partitioning method in order to fully of roads. This method leads to unbiased road layouts in forest areas. The size of the unit partitiones area can be calculated as a function of the predefined road density. In addition, the user-defined road density of the area-partitioning method provides flexibility in applying the model to real situations. The rational road network can be easily achived for varying road densities, which would be an essential element for network design of forest roads. The optimality conditions are evaluated in conjuction with longitudinal gradients, investment efficiency earthwork quantity or the mixed criteria of these three. The performance of the model was measured and, then, compared with those of conventional ones in terns of average skidding distance, accessibility of stands, development index and circulated road network index. The results of the performance analysis indicate that selection of roading routes for network design using the digital terrain analysis and the area-partitioning method improves performance of the network design medel.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.121-125
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• 2007

Recently, constructing and complementing the road network database are a main key in all social operation in our life. However it needs high expenses for constructing and complementing the data, and relies on many people for finishing the tasks. This study propose a novel method to extract urban road networks from 3-D LiDAR data automatically. This method integrates height, reflectance, and clustered road point information. Geometric information of general roads is also applied to cluster road points group correctly. The proposed method has been tested on various urban areas which contain complicated road networks. The results conclude that the integration of height, reflectance, and geometric information worked reliably to cluster road points.

• Journal of KIISE:Software and Applications
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• v.28 no.2
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• pp.132-140
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• 2001

야외에서 인지되는 자연 경치는 다양한 개체, 빛의 산란, 또는 변화를 주는 많은 요소들 때문에 컴퓨터 영상처리에서 인식하기가 쉽지 않다. 본 논문에서는 다층 인지 신경망을 이용하여 도로가 포함된 야외영상에 나타나는 개체들을 인식하는 방법을 연구하였다. 자연 영상을 영역화한 후, 각각의 영역들에 대하여 색상과 기하학적인 특성에 근거하여 특성벡터를 추출하고 이를 신경망에 입력하여 각 영역을 구분하는 2단계의 알고리듬을 제안한다. 먼저 야외 영상들을 개선된 영역 확장법과 병합과정에 의하여 개체별로 영역화하였다. 영역화된 연상은 자연 영상과 함께 영상 데이타베이스에 저장되고, 이 자료들을 이용하여 각 영역의 특성벡터를 계산하였다. 이 특성 벡터를 구성된 신경망의 입력층에 전달하면, 각 영역은 27개의 개체 중의 하나로 출력층에서 인식된다. 제안된 방법은 학습에 사용된 데이타, 학스베 사용되지 않은 새로운 데이타, 그리고 모두 합하여 놓은 데이타의 세가지 데이타 군에서 무작위로 선별하여 인식률을 측정하였다. 학습된 데이타에서는 99.4%까지의 인식률을 보여주었고, 학습되지 않은 데이타에 대해서도 최고 89.1%까지의 인식률을 나타내었다. 제안된 방법은 평균적으로 88.1%~97.9%의 인식률을 보여주어 자연 경치의 인식에 신뢰성이 있는 방법으로 사용될 수 있음을 증명하였다.

• Spatial Information Research
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• v.13 no.3 s.34
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• pp.195-209
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• 2005

Forest road has been an essential infrastructure for various forestry practices as well as for recreational use, disaster management, and local economics promotion. Since 1980s, extensive network of forest roads has been constructed as an national project in Korea. However, due to the minimal-budget of the project, accurate maps of forest road are not usually available. Although forest road map is a main thematic layer for the forest Geographic Information System (FGIS), its locational accuracy has not been sufficient for the practical applications and, therefore, the update of digital forest road maps is urgent. The objectives of this study is to compare ae methodology of generating and updating digital forest road maps from the aspects of the map accuracy and the efficiency of methods. Four mapping methods (GPS surveying, satellite imagery, ortho aerial photograph, and digital photogrammetry) were applied to generate the forest road maps over the study area of Mt. Oseo in Chungchungnam-do, which has a 35km forest roads distributed in national, public and private forests. The forest road Imp produced by digital photogrammetric method is the most accurate and comparable to GPS surveying although it required the greatest amount of labor time.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.6
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• pp.71-79
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• 2016

Evaluation of the walkability of the urban pedestrian network requires construction of a pedestrian network model that reflects Space Syntax Theory. Space Syntax Theory deduces an integration value through which materials for evaluation of the pedestrian network's connectivity can be produced; and can aid in illustrating the ease of walkability through the model's calculation of pedestrian indices such as movability, comfort, and safety. But the representation of space syntax theory in the pedestrian network requires that turn delay be added by means of a network-type construction method. While tree-based Dial Algorithm proposed for the logit-based probability walkability distribution model may be effective for link-based pedestrian volume distribution, it requires further network expansion to reflect turn delays. In this research, Vine-based Dial Algorithm is executed in order to obtain a measure reflecting the integration value for Space Syntax Theory. The Vine-based Dial Algorithm of two adjacent links, which forms the minimum unit of the Vine network, has the advantage of encompassing turn delay, and thus eliminates the need for network expansion. Usage of the model to evaluation of complicated pedestrian spheres such as urban roads is left to further research. Especially the progression of the proposed method is deduced through case study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.84-99
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• 2021

As the micro-mobility market grows, the demand for route guidance, that includes uphill information as well, is increasing. Since the climbing angle depends on the electric motor uesed, it is necessary to establish an uphill road DB according to the threshold standard. Although road alignment information is a very important element in the basic information of the roads, there is no information currently on the longitudinal slope in the road digital map. The High Definition(HD) map which is being built as a preparation for the era of autonomous vehicles has the altitude value, unlike the existing standard node link system. However, the HD map is very insufficient because it has the altitude value only for some sections of the road network. This paper, hence, intends to propose a method to generate the road longitudinal slope using currently available data. We developed a method of computing the longitudinal slope by combining the digital elevation model and the standard link system. After creating an altitude at the road link point divided by 4m based on the Seoul road network, we calculated individual slope per unit distance of the road. After designating a representative slope for each road link, we have extracted the very steep road that cannot be climbed with personal mobility and the slippery roads that cannot be used during heavy snowfall. We additionally described errors in the altitude values due to surrounding terrain and the issues related to the slope calculation method. In the future, we expect that the road longitudinal slope information will be used as basic data that can be used for various convergence analyses.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.4
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.