• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.123-129
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• 2006

High-resolution aerial color image offers great possibilities for geometric and semantic information for spatial data generation. However, shadow casts by buildings and trees in high-density urban areas obscure much of the information in the image giving rise to potentially inaccurate classification and inexact feature extraction. Though many researches have been implemented for solving shadow casts, few studies have been carried out about the extraction of features hindered by shadows from aerial color images in urban areas. This paper presents a asphalt road boundary extraction technique that combines information from aerial color image and LIDAR (LIght Detection And Ranging) data. The following steps have been performed to remove shadow effects and to extract road boundary from the image. First, the shadow regions of the aerial color image are precisely located using LEAR DSM (Digital Surface Model) and solar positions. Second, shadow regions assumed as road are corrected by shadow path reconstruction algorithms. After that, asphalt road boundary extraction is implemented by segmentation and edge detection. Finally, asphalt road boundary lines are extracted as vector data by vectorization technique. The experimental results showed that this approach was effective and great potential advantages.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.535-544
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• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.113-122
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• 2024

In this paper, we conducted a thorough investigation of existing simulators for running simulations of Vehicular Adhoc Networks (VANET) in realistic road environments, such as digital twins. After careful consideration, we chose a simulator that combines OSM (OpenStreetMap), SUMO (Simulation of Urban MObility), and OMNeT++ due to its open-source nature and efficient performance. Using this integrated simulator, we carried out VANET simulations in both simple virtual road environments and realistic road environments. Our findings revealed significant differences in VANET performance between the two types of environments, emphasizing the need to consider realistic road and traffic environments for reliable VANET operation. Furthermore, our simulations demonstrated significant performance variability, with performance degradation observed as vehicle density decreased and dynamic changes in network topology increased. These results underscore the importance of digital twin-based approaches in VANET research, highlighting the need to simulate real-world road and traffic conditions rather than relying on simple virtual road environments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.75-81
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• 2012

Road identification is the important task for extracting a road region from the high-resolution satellite images, when the road candidates is extracted by the pre-processing tasks using a binarization, noise removal, and color processing. Therefore, we propose a noble approach for identifying a road using the orientation-selective spatial filters, which is motivated by a computational model of neuron cells found in the primary visual cortex. In our approach, after the neuron cell typed spatial filters is applied to the isolated connected-labeling road candidate regions, proposed method identifies the region of perceiving the strong orientation feature with the real road region. To evaluate the effectiveness of the proposed method, the accuracy&error ratio in the confusion matrix was measured from road candidates including road and non-road class. As a result, the proposed method shows the more than 92% accuracy.

• Journal of Korea Spatial Information System Society
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• v.9 no.3
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• pp.51-65
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• 2007

In order to analyze moving object trajectories on road networks, its representation needs to be defined correctly. The most previous methods representing moving object trajectories on road networks defined moving object trajectories as a set of passed location and its time. It is required much time in processing analysis such as retrieval for moving object trajectories. In this paper, we focus on POI(Points of Interest) on road networks and propose methods simplifying moving object trajectories based on it. Our method simplifies moving object trajectories by reducing the number of POIs that moving object trajectories passed and maintains its form after moving object trajectories were simplified.

• Journal of the Society of Disaster Information
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• v.13 no.2
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• pp.191-198
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• 2017

Lego block system forms a temporary pavement area using the assembled structure block in the road excavation work for the maintenance and installation of facilities. This system was developed for a safe passage of vehicles and pedestrians. A study on the fundamental performance of a lego block system was performed through material quality, sliding resistance and compression tests. And compaction performance of ground on the road was reviewed and evaluated through field tests. As a result, a lego block system for road recovery showed the excellent performance and compaction effect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.143-148
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• 2008

This Study is about driving simulation after road design by 3D-GIS in digital elevation model from digital aerial photogrammetry. For designing roads efficiently it's very important to consider geographical features before design when analyze the view. Nevertheless, existing studies is mainly restricted in the mountainous, despite of using digital map or aerial photogrammetry and the study which used aerial photo in the area where the road designing is made really is not get executed. Therefore, this study will do 3D-road design and driving simulation by appling really road design data to topography, on the basis of digital elevation generated from aerial photogrammetry.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.3
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• pp.233-248
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• 2012

The national emission from energy sector accounted for 84.7% of all domestic emissions in 2007. Of the energy-use emissions, the emission from mobile source as one of key categories accounted for 19.4% and further the road transport emission occupied the most dominant portion in the category. The road transport emissions can be estimated on the basis of either the fuel consumed (Tier 1) or the distance travelled by the vehicle types and road types (higher Tiers). The latter approach must be suitable for simultaneously estimating $CO_2$, $CH_4$, and $N_2O$ emissions in local administrative districts. The objective of this study was to estimate 31 municipal GHG emissions from road transportation in Gyeonggi Province, Korea. In 2008, the municipalities were consisted of 2,014 towns expressed as Dong and Ri, the smallest administrative district unit. Since mobile sources are moving across other city and province borders, the emission estimated by fuel sold is in fact impossible to ensure consistency between neighbouring cities and provinces. On the other hand, the emission estimated by distance travelled is also impossible to acquire key activity data such as traffic volume, vehicle type and model, and road type in small towns. To solve the problem, we applied a hierarchical cluster analysis to separate town-by-town road patterns (clusters) based on a priori activity information including traffic volume, population, area, and branch road length obtained from small 151 towns. After identifying 10 road patterns, a rule building expert system was developed by visual basic application (VBA) to assort various unknown road patterns into one of 10 known patterns. The expert system was self-verified with original reference information and then objects in each homogeneous pattern were used to regress traffic volume based on the variables of population, area, and branch road length. The program was then applied to assign all the unknown towns into a known pattern and to automatically estimate traffic volumes by regression equations for each town. Further VKT (vehicle kilometer travelled) for each vehicle type in each town was calculated to be mapped by GIS (geological information system) and road transport emission on the corresponding road section was estimated by multiplying emission factors for each vehicle type. Finally all emissions from local branch roads in Gyeonggi Province could be estimated by summing up emissions from 1,902 towns where road information was registered. As a result of the study, the GHG average emission rate by the branch road transport was 6,101 kilotons of $CO_2$ equivalent per year (kt-$CO_2$ Eq/yr) and the total emissions from both main and branch roads was 24,152 kt-$CO_2$ Eq/yr in Gyeonggi Province. The ratio of branch roads emission to the total was 0.28 in 2008.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.355-360
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• 2017

Recently, with the development of autonomous vehicle technology, the importance of precise road maps is increasing. A precise road map is a digital map with lane information, regulations, safety information, and various road facilities. Conventional precise road maps have been tested and developed based on the mobile mapping system (MMS). But they have not been activated due to high introduction costs. However, in the case of unmanned aerial vehicles (UAVs), the application field is continuously increasing. This study tries to extract information through classification of high-resolution UAV images for autonomous driving. Autonomous vehicle test roads were selected as study sites, and high-resolution orthoimages were produced using UAVs. In addition, the utilization of high-resolution orthoimages has been proposed by effectively extracting data for precise road map construction, such as road lines, guards, and machines through image classification. If additional experimentation and verification are performed, the field of UAV image use will be expanded, providing the data to automobile manufacturers and related public and private organizations, and venture companies will contribute to the development of domestic autonomous vehicle technology.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.