• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.5
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• pp.535-546
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• 2020

Land cover maps are derived from satellite and aerial images by the Ministry of Environment for the entire Korea since 1998. Even with their wide application in many sectors, their usage in research community is limited. The main reason for this is the map compilation cycle varies too much over the different regions. The situation requires us a new and quicker methodology for generating land cover maps. This study was conducted to automatically generate land cover map using aerial ortho-images and Landsat 8 satellite images. The input aerial and Landsat 8 image data were trained by Residual U-Net, one of the deep learning-based segmentation techniques. Study was carried out by dividing three groups. First and second group include part of level-II (medium) categories and third uses group level-III (large) classification category defined in land cover map. In the first group, the results using all 7 classes showed 86.6 % of classification accuracy The other two groups, which include level-II class, showed 71 % of classification accuracy. Based on the results of the study, the deep learning-based research for generating automatic level-III classification was presented.

• Spatial Information Research
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• v.20 no.6
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• pp.129-137
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• 2012

For construction project in extreme cold region, it is essential to establish basic data on the site such as topographical data from the early stage of construction of planning and designing, and it is needed to frequently perform site investigation when necessary. However, extreme cold regions are characteristic of being at long distance and difficult in approaching, and special regions such as Antarctica, in particular, are hard to conduct site investigation. Although a site investigation may be conducted, those who can visit Antarctica are sufficiently limited so that most of the staff may participate in construction without knowledge of the site and increase the risk of errors in decision making or designing. In order to resolve such problems, the authors in this study identified methods of building wide-area topographical data and bedrock classification data of exposed areas via remote sensing and of building precise topographical data on the construction site. Also, the authors attempted to present methods by which such data can be managed and visualized integrally via three-dimensional GIS technology and all the participants in construction can learn sense of field and conduct necessary analysis as frequent as possible. The areas around the Jangbogo Antarctic Station were selected to be the research area for conducting effective integrational management and three-dimensional visualization of various spatial data such as wide-area digital elevation model, ortho-images, bedrock classification data, local precise digital elevation model, and site images. The results of this study may enable construction firms to analyze local environments for construction whenever they need for construction in extreme cold regions and then support construction work including decision making or designing.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.2
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• pp.127-136
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• 2002

DEM(Digital Elevation Model) is used widely in image processing, water resources, construction, GIS, landscape architecture, telecommunication, military operations and other related areas. And it is used especially in producing ortho-photo based on specific DEM and developing 3D GIS database vividly. As LiDAR(Light and Detection And Ranging) system emerged recently, DEM could be developed in urban area more efficiently and more economically, compared to the conventional DEM Production. Traditional method using check points for elevation has tome limitations in structure's height accuracy by LiDAR, because it uses only terrain height. Accordingly after the downtown of Chungju city was selected as a test field in this paper and DEM and digital ortho images was produced by way of LiDar survey, the accuracy was evaluated through analytical plotting map. The result shows that in case of buildings in LiDAR DEM, the accuracy is 0.30 m in X, 0.62 m in Y and RMS is 1.17 m. The difference distribution between DEM and plotting map in range of $\pm$10 cm was 36.2% and $\pm$10 cm $\pm$20 cm was 43.53%. The accuracy of LiDAR in this study meets 1/5,000 which is the regulation for map of NGI(National Geography Institute) and LiDAR can be possibly used in many other applied area.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1479-1488
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• 2022

Accidents in which oil spills occur intermittently in the ocean due to ship collisions and sinkings. In order to prepare prompt countermeasures when such an accident occurs, it is necessary to accurately identify the current status of spilled oil. To this end, the Coast Guard patrols the target area with a fixed-wing airplane or helicopter and checks it with the naked eye or video, but it was difficult to determine the area contaminated by the spilled oil and its exact location on the map. Accordingly, this study develops a technology for direct ortho-rectification by automatically geo-referencing aerial images collected by the Coast Guard without individual ground reference points to identify the current status of spilled oil. First, meta information required for georeferencing is extracted from a visualized screen of sensor information such as video by optical character recognition (OCR). Based on the extracted information, the external orientation parameters of the image are determined. Images are individually orthorectified using the determined the external orientation parameters. The accuracy of individual orthoimages generated through this method was evaluated to be about tens of meters up to 100 m. The accuracy level was reasonably acceptable considering the inherent errors of the position and attitude sensors, the inaccuracies in the internal orientation parameters such as camera focal length, without using no ground control points. It is judged to be an appropriate level for identifying the current status of spilled oil contaminated areas in the sea. In the future, if real-time transmission of images captured during flight becomes possible, individual orthoimages can be generated in real time through the proposed individual orthorectification technology. Based on this, it can be effectively used to quickly identify the current status of spilled oil contamination and establish countermeasures.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1693-1705
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• 2023

Rural areas, which account for about 90% of the country's land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents' lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents' lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.277-292
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• 2020

The purpose of this study is to propose the possibility of precise surface sedimentary facies classification and a more accurate classification method by generating the large-scale map of surface sedimentary facies based on UAV data and object-based image analysis (OBIA) for Hwang-do tidal flat in Cheonsu bay. The very high resolution UAV data extracted factors that affect the classification of surface sedimentary facies, such as RGB ortho imagery, Digital elevation model (DEM), and tidal channel density, and analyzed the principal components of surface sedimentary facies through statistical analysis methods. Based on principal components, input data to be used for classification of surface sedimentary facies were divided into three cases such as (1) visible band spectrum, (2) topographical elevation and tidal channel density, (3) visible band spectrum and topographical elevation, tidal channel density. The object-based image analysis classification method was applied to map the classification of surface sedimentary facies according to conditions of input data. The surface sedimentary facies could be classified into a total of six sedimentary facies following the folk classification criteria. In addition, the use of visible band spectrum, topographical elevation, and tidal channel density enabled the most effective classification of surface sedimentary facies with a total accuracy of 63.04% and the Kappa coefficient of 0.54.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.1
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• pp.105-113
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• 2011

Since existing thematic maps have been made with medium- to low-resolution satellite images, they have several shortcomings including low positional accuracy and low precision of presented thematic information. Digital aerial photo image taken recently can express panchromatic and color bands as well as NIR (Near Infrared) bands which can be used in interpreting forest areas. High resolution images are also available, so it would be possible to conduct precision land cover classification. In this context, this paper implemented object-based land cover classification by using digital aerial photos with 0.12m GSD (Ground Sample Distance) resolution and IKONOS satellite images with 1m GSD resolution, both of which were taken on the same area, and also executed qualitative analysis with ortho images and existing land cover maps to check the possibility of object-based land cover classification using digital aerial photos and to present usability of digital aerial photos. Also, the accuracy of such classification was analyzed by generating TTA(Training and Test Area) masks and also analyzed their accuracy through comparison of classified areas using screen digitizing. The result showed that it was possible to make a land cover map with digital aerial photos, which allows more detailed classification compared to satellite images.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1517-1526
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• 2021

With the recent global and domestic development of Earth observation satellites, the applications of satellite images have been widened. Research for improving the geometric accuracy of satellite images is being actively carried out. This paper studies the possibility of automated ground control point (GCP) generation for CAS-4 satellite, to be launched in 2025 with the capability of image acquisition at 5 m ground sampling distance (GSD). In particular, this paper focuses to check whether GCP chips with 25 cm GSD established for CAS-1 satellite images can be used for CAS-4 and to check whether optimalspatial resolution for matching between CAS-4 images and GCP chips can be determined to improve matching performance. Experiments were carried out using RapidEye images, which have similar GSD to CAS-4. Original satellite images were upsampled to make satellite images with smaller GSDs. At each GSD level, up-sampled satellite images were matched against GCP chips and precision sensor models were estimated. Results shows that the accuracy of sensor models were improved with images atsmaller GSD compared to the sensor model accuracy established with original images. At 1.25~1.67 m GSD, the accuracy of about 2.4 m was achieved. This finding lead that the possibility of automated GCP extraction and precision ortho-image generation for CAS-4 with improved accuracy.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.201-206
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• 2019

Korea is constructing geospatial information application system for geospatial information utilization, but it is trying to establish a system for joint use of geospatial information system centering on Ministry of Land Transport and Transport due to the problem of sharing. The purpose of this study is to investigate and analyze the geospatial information application system operated by local governments, and to suggest the application of geospatial information application system using unmanned aerial images. As a result of the research, it was found that the functions of existing spatial information application system are concentrated on the public services and it is difficult to share and utilize data between administrative departments. In addition, the utilization of the system using unmanned aerial image has been suggested, and additional functions such as vector display, area calculation, and report generation have been derived to improve the usability of geospatial information application system. If additional functions of spatial information application system are added through further studies in the future, it will be possible to use it as a basic data of field survey and policy decision in related fields. And non-experts will be able to improve the efficiency of work by utilizing highly accurate geospatial information in various fields.