• Spatial Information Research
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• v.10 no.3
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• pp.469-480
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• 2002

Nowadays, digital maps of 1:5,000 scale are used to plan and review far road alignment design. However, the updating and modifying period of digital maps is not so harmonious as topographical changes caused by rapid developments can be reflected in digital maps, the different areas between real surface and digital map can be found easily. This research is aimed to suggest that the use of high resolution satellite image is effective way to get latest topographical information for road alignment design about wide region. IKONOS satellite images were geometrically corrected, and the road alignment data previously designed by traditional procedure were overlapped on the satellite images. As a result, the satellite image maps clearly described wrong road alignment, and modification of road alignment could be accomplished adequately By these procedures, road alignment design was Improved in quality, and could be reasonable and economic design to prevent modification that would be happened in the next step of practical plan. For the geometric correction method of IKONOS images, Thin Plate Spline(TPS) transformation with large number of ground control points, as well as ortho rectification, was effective.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.239-247
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• 2022

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.545-550
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• 2004

Chilli reservoir is agricultural purpose reservoir made in 1945. But its bottom is covered with thick accumulated sewage layer, so its original function has failed. This study conducted cadastral state surveying to survey the state of this area prior to dredging the accumulated bottom sewage layer of Chill reservoir. Prior to cadastral state surveying we analyzed the digital map and aerial photographs of study area. In cadastral state surveying, traversed network was constructed on the road and banks near the reservoir, a surveyed map was made by surveying with Auto-reduction EDM Alidade. From the comparison results of the cadastral map and digital map, we found small chilli reservoir is expressed as rice field on the digital map, and it should be corrected. And when we make digital map and topographic map, the errors will be diminished if we use the cadastral map at the same time.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1144-1144
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• 2007

• Proceedings of the CALSEC Conference
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• 1999.07b
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• pp.711-716
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• 1999

조직내의 정형, 비정형 정보를 생성하여 분배, 유통, 축적, 재활용의 과정을 반복하여 조직의 지적 자산을 구축하여 활용토록 하는 시스템 ㆍ 광의의 GroupWare ㆍ 조직 내에 내재된 Information들을 관계되는 지식으로 편리하게 사용하고, 손쉬운 검색이 가능하도록 Multidimensional Road Map 혹은 Navigational Map을 제공하는 도구(중략)

• Journal of KIISE:Databases
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• v.35 no.5
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• pp.447-458
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• 2008

In this paper, we address an efficient processing scheme for k-nearest neighbor queries to retrieve k static objects in road network databases. Existing methods cannot expect a query processing speed-up by index structures in road network databases, since it is impossible to build an index by the network distance, which cannot meet the triangular inequality requirement, essential for index creation, but only possible in a totally ordered set. Thus, these previous methods suffer from a serious performance degradation in query processing. Another method using pre-computed network distances also suffers from a serious storage overhead to maintain a huge amount of pre-computed network distances. To solve these performance and storage problems at the same time, this paper proposes a novel approach that creates an index for moving objects by approximating their network distances and efficiently processes k-nearest neighbor queries by means of the approximate index. For this approach, we proposed a systematic way of mapping each moving object on a road network into the corresponding absolute position in the m-dimensional space. To meet the triangular inequality this paper proposes a new notion of average network distance, and uses FastMap to map moving objects to their corresponding points in the m-dimensional space. After then, we present an approximate indexing algorithm to build an R*-tree, a multidimensional index, on the m-dimensional points of moving objects. The proposed scheme presents a query processing algorithm capable of efficiently evaluating k-nearest neighbor queries by finding k-nearest points (i.e., k-nearest moving objects) from the m-dimensional index. Finally, a variety of extensive experiments verifies the performance enhancement of the proposed approach by performing especially for the real-life road network databases.

• The Transactions of the Korea Information Processing Society
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• v.1 no.4
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• pp.452-460
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• 1994

Maps are one of the most complicated types of drawings. Drawing recognition technology is not yet sophisticated enough for automated map reading To automatically extract a road map directly from more complicated topographical maps, a very complicated algorithm is needed, since the image generally involves such complicated patterns as symbols, characters, residential sections, rivers, railroads, etc. This paper describes a new feature extraction method based on the human optical neural field. We apply this method to extract complete set of road segments from topographical maps. The proposed method successfully extract road segments from various areas.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.79-82
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• 2005

Recently the necessity of road data is still being increased in industrial society, so there are many repairing and new constructions of roads at many areas. According to the development of government, city and region, the update and acquisition of road data for GIS (Geographical Information System) is very necessary. In this study, the fusion method with range data(3D Ground Coordinate System Data) and Intensity data in stand alone LiDAR data is used for road extraction and then digital image processing method is applicable. Up to date Intensity data of LiDAR is being studied. This study shows the possibility method for road extraction using Intensity data. Intensity and Range data are acquired at the same time. Therefore LiDAR does not have problems of multi-sensor data fusion method. Also the advantage of intensity data is already geocoded, same scale of real world and can make ortho-photo. Lastly, analysis of quantitative and quality is showed with extracted road image which compare with I: 1,000 digital map.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.6
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• pp.726-732
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• 2013

It is required for UGV(Unmanned Ground Vehicle) to have a LPP(Local Path Plan) component which generate a local path via the center of road by analyzing binary map to travel autonomously unpaved road in rough environment. In this paper, we present the method of boundary estimation for unpaved road and a local path planning method based on RANGER algorithm using the estimated boundary. In specially, the paper presents an approach to estimate road boundary and the selection method of candidate path to minimize the problem of zigzag driving based on Bayesian probability reasoning. Field test is conducted with scenarios in rough environment in which bush, tree and unpaved road are included and the performance of proposed method is validated.

• ETRI Journal
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• v.37 no.6
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• pp.1220-1230
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• 2015

An autonomous valet parking (AVP) system is designed to locate a vacant parking space and park the vehicle in which it resides on behalf of the driver, once the driver has left the vehicle. In addition, the AVP is able to direct the vehicle to a location desired by the driver when requested. In this paper, for an AVP system, we introduce technology to recognize a parking space using image sensors. The proposed technology is mainly divided into three parts. First, spatial analysis is carried out using a height map that is based on dense motion stereo. Second, modelling of road markings is conducted using a probability map with a new salient-line feature extractor. Finally, parking space recognition is based on a Bayesian classifier. The experimental results show an execution time of up to 10 ms and a recognition rate of over 99%. Also, the performance and properties of the proposed technology were evaluated with a variety of data. Our algorithms, which are part of the proposed technology, are expected to apply to various research areas regarding autonomous vehicles, such as map generation, road marking recognition, localization, and environment recognition.