• Journal of Korea Spatial Information System Society
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• v.4 no.2 s.8
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• pp.55-64
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• 2002

VEfficient generation of road data is one of the most important issues in GIS (Geographic Information System). In this paper, we propose a hybrid approach for automatic generation of road data by combining mobile mapping and image processing techniques. Mobile mapping systems have a form of vehicle equipped with CCD camera, GPS, and INS. They can calculate absolute position of objects that appear in acquired image by photogrammetry, but it is labor-intensive and time-consuming. Automatic road detection methods have been studied also by image processing technology. However, the methods are likely to fail because of obstacles and exceptive conditions in the real world. To overcome the problems, we suggest a hybrid method for automatic road generation, by exploiting both GPS/INS data acquired by mobile mapping system and image processing algorithms. We design an estimator to estimate 3-D coordinates of road line and corresponding location in an image. The estimation process reduces complicated image processing operations that find road line. The missing coordinates of road line due to failure of estimation are obtained by cubic spline interpolation. The interpolation is done piecewise, separated by rapid change such as road intersection. We present experimental results of the suggested estimation and interpolation methods with image sequences acquired by mobile mapping system, and show that the methods are effective in generation of road data.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1258-1261
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• 2017

1급 발암물질인 미세먼지 중 44.3%를 차지하고 있는 도로 비산먼지는 효과적인 미세먼지 농도 저감 대책의 방안 중 하나이다. 도로 비산먼지 제거는 일반적으로 특수 차량을 이용, 정해진 경로와 주기에 따라 운행된다. 이러한 운행방식은 도로의 오염 현황에 따른 효과적 경로 선정 및 운영이 어렵다. 본 논문에서는 도로 비산먼지 제거의 효율적인 경로 제안을 위해 대구지역에 분포된 KISTI 이동형 도시센싱 테스트베드에서 수집되는 고해상도의 실시간 지역별 오염 현황 데이터를 활용하여 실시간 오염도를 분석하고, LSTM(LONG SHORT-TERM MEMORY) 알고리즘을 활용하여 미래의 미세먼지 농도를 예측하였다. 기존 연구와 달리 지역별 상황을 고려한 데이터를 사용하여 선형 회귀 분석을 수행하였다. 실험 결과, 시간 속성을 고려한 LSTM이 MLP 보다 평균 제곱근 오차 값이 경우에 따라 최대 30% 더 작음을 확인했다. 본 연구를 기반으로 고해상도 사물 데이터 기반 예측 연구의 가능성을 보였으며, 미세먼지 예측 결과를 활용 유연하고 효과적인 도로 청소차량의 운행 경로를 설정에 활용될 수 있을 것으로 기대한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.18 no.4
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• pp.325-333
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• 2000

This paper deals with real time building of route and vertical alignment information system for the efficient and scientific management using GSIS. First, we redesigned route and vertical alignment for obtained basic data using road projector(road design main software). Using by these acquired result, we made a database on road plan and vertical alignment drawing as a basic information being used to construction of road complex information system. The main objectives in this study are the rational maintenance road planing for drawings, its database and the systematization of route information management through the fast updating, mending, supplement. Result of this study enables us to manage route information.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.27-30
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• 2021

경제 성장과 함께 자동차의 수요가 늘어남에 따라 교통사고 발생 빈도는 꾸준히 증가하고 있다. 이에, 본 연구에서는 교통사고를 야기하는 도로 및 기상환경과 같은 조건을 활용하여 기계학습 모델을 통해 서울시 교통사고 사상자 수를 예측하는 모형을 찾고자 한다. 활용한 데이터는 도로교통 공단에서 제공하는 교통사고 사상자 수 정보를 포함하는 데이터로 2015년부터 2018년도까지 데이터를 학습에 사용하였고 2019년도 데이터를 테스트 평가에 사용하였다. 실증연구를 통해 트리 기반의 모델 별 성능을 비교하였으며 본 연구에 대한 결과는 사고 발생 시 우선순위에 의한 구조활동이 가능하게 함과 도로상황 및 기상을 고려한 안전운전 가이드 지식으로 활용될 수 있다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.255-262
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• 2008

Value of natural environment and the importance of conservation are augmented gradually, and collision of environment conservation and development are caused in various construction industries. In this study, Presented practical use way to ecological road design using vegetation information and high precision 3-dimensional geo-spatial data for minimizing pollution. Also, analyzed freezing danger of road surface in winter and direct ray of light danger through simulation of completed road and surrounding environment. And presented road design support way through view analysis.

• Spatial Information Research
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• v.15 no.2
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• pp.95-109
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• 2007

Recently people have been offered location based services on road networks. The navigation system, one of applications, serves to find the nearest gas station or guide divers to the shortest path based 2D map. However 3D map is more important media than 2D map to make sense friendly for the real. Although 3D map's data size is huge, portable devices' storage space is small. In this paper, we define continuous perspective queries to support 3D map to mobile user on road networks and propose this queries processing method.

• 한국도로학회지:도로
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• v.20 no.1
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• pp.48-56
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• 2018

• 한국도로학회지:도로
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• v.19 no.4
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• pp.47-54
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• 2017

• 한국도로학회지:도로
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• v.17 no.4
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• pp.56-61
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• 2015

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.1-6
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• 2021

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.