• Journal of the Society of Disaster Information
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• v.14 no.4
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• pp.501-509
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• 2018

Purpose: UAV-based photo measurements are being researched using UAVs in the space information field as they are not only cost-effective compared to conventional aerial imaging but also easy to obtain high-resolution data on desired time and location. In this study, the UAV-based high-resolution images were used to perform the land cover classification. Method: RGB cameras were used to obtain high-resolution images, and in addition, multi-distribution cameras were used to photograph the same regions in order to accurately classify the feeding areas. Finally, Land cover classification was carried out for a total of seven classes using created ortho image by RGB and multispectral camera, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix) using RF (Random Forest), a representative supervisory classification system. Results: To assess the accuracy of the classification, an accuracy assessment based on the error matrix was conducted, and the accuracy assessment results were verified that the proposed method could effectively classify classes in the region by comparing with the supervisory results using RGB images only. Conclusion: In case of adding orthoimage, multispectral image, NDVI and GLCM proposed in this study, accuracy was higher than that of conventional orthoimage. Future research will attempt to improve classification accuracy through the development of additional input data.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.206-217
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• 2021

Purpose: Recently, a series of large social disasters have led to a lot of research to prevent social disasters as well as natural disasters and reduce damage. However, this paper aims to select the types of social disasters that local governments should focus on and create basic data for effective countermeasures and mitigation efforts. Method: Among 43 types of disasters announced by the Ministry of Public Administration and Security, 11 types of disasters were selected and collected to select the main types of disasters, and risk types were derived by region with risk maps. In order to derive the risk map, each detailed index was rescheduled to be 0-1 and weights were determined through entropy technique. Result: As a result, about 41% of the major disasters announced by the Ministry of Public Administration and Security were consistent, and the rest of the major types were disasters that could not be obtained or have not occurred in the past 20 years. Conclusion: Therefore, in order to establish an effective prevention and recovery plan for social disasters through this study, it was intended to present social disaster-focused disasters for each local government.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.697-705
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• 2020

This paper deals with an analysis of crustal movement for the sourthern part of Korean peninsula using GNSS (Global Navigation Satellite System) data. An earthquake of more than 5.0 occurred in the southeastern region of the Korean Peninsula, and it is necessary to evaluate the risk of earthquakes in various ways.In order to reveal long-term tectonic movement patten in Pohang and Gyeongju provinces, we derived crustal movement parameters related with elastic theory. We used GAMIT/GLOBK for analyzing seven-year interval GNSS data of CORS (Continuously Operating Reference Stations). The azimuth of velocity vectors trended generally about 110° with an mean magnitude of 31mm/yr.The main characteristics of the strain change for seven-year in Korea obtaind from our study. Direction of the principal axis of the maximum compression is ENE-WSW as a whole, through there are some exceptions. The mean rate of the maximum shear strain change is (0.11±0.07)μ/yr, that is approximately one third that of Chubu district, Central Japan. Taking into account our results, the mean rate of maximum shear in southern part of Korean peninsula is considered as reasonable. The mean azimuth of principal strain is about (85.4°±26.8°). There are some exceptions of azimuth because the average azimuth differ from the left and right side in Yangsan fault which are about (73.2°±21.5°) and (105.2°±17.0°) respectively, It is noteworthy that the high seismicity areas in the southern part of Korea peninsula almost coincides with the area of large strain rate. As a conclusion, it could be stated that the our study represents the characteristics of crustal deformation in the southern part of peninsula, and contributes to the researches on earthquake disaster management.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.60-66
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• 2020

Torrential rain causes landslide damage every year. In particular, the 2011 downpour caused landslides at numerous points throughout Mt. Woomyeon, which resulted in considerable damage to people and property. Because it occurred in an urban area, this case became a major social issue and received public attention. Measures were quickly implemented for multilateral investigations and recovery. Landslides caused by heavy rain are greatly affected by rainfall at the time. Landslides from the upper part erode the flow path, increasing the size, causing much damage to the lower part. This study selected a rural village area among the damaged areas of Mt. Woomyeon, and analyzed the change in terrain profile before and after a landslide using the DSM data obtained from airborne LiDAR. This area can be divided into three hydrological basins. For each basin, the analysis was performed on the average slope of each part of the flow path, as well as the erosion and deposition due to soil flow. As a result of the analysis, it was estimated that the total amount of soil from the Jeonwon village was 15,300㎥. These field data based on GIS can be used as basic information to predict damage in the case of a similar disaster, and it can be helpful in analyzing the results of various debris flow simulations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.41-46
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• 2021

As data acquisition using unmanned aerial vehicles is widely used, as one of the ways to increase the accuracy of photogrammetry using unmanned aerial vehicles, a method of inputting both vertical and oblique images in bundle adjustment of aerial triangulation has been proposed. In this study, in order to find a suitable method for increasing the accuracy of photogrammetry, the accuracy of the case of adjusting the oblique images taken at different shooting angles and the case of adjusting the oblique images with different shooting angles at the same time with the vertical images were compared. As a result of the study, it was found that the error of the checkpoint decreases as the angle of the input oblique images increases. In particular, when the vertical images and oblique images are used together, the height error decreases significantly as the angle of the oblique images increases. The current 『Aerial Photogrammetry Work Regulation』 requires RMSE (Root Mean Square Error), which is the same as GSD (Ground Spatial Distance) of a vertical image. When using an oblique images with a shooting angle of 50°, a result close to this standard is obtained. If the vertical images and the 50° oblique images were adjusted at the same time, the work regulations could be satisfied. Using the results of this study, it is expected that photogrammetry using low-cost cameras mounted on unmanned aerial vehicles will become more active.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.217-226
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• 2022

In the view of the application of high-precision spatial information production, UAV (Umanned Aerial Vehicle)-Photogrammetry has a problem in that it lacks specific procedures and detailed regulations for quantitative quality verification methods or certification of captured images. In addition, test tools for UAV image quality assessment use only the GSD (Ground Sample Distance), not MTF (Modulation Transfer Function), which reflects image resolution and contrast at the same time. This fact makes often the quality of UAV image inferior to that of manned aerial image. We performed MTF and GSD analysis simultaneously using a siemens star to confirm the necessity of MTF analysis in UAV image quality assessment. The analyzing results of UAV images taken with different payload and sensors show that there is a big difference in σ_MTF values, representing image resolution and the degree of contrast, but slightly different in GSD. It concluded that the MTF analysis is a more objective and reliable analysis method than just the GSD analysis method, and high-quality drone images can only be obtained when the operator make images after judging the proper selection the sensor performance, image overlaps, and payload type. However, the results of this study are derived from analyzing only images acquired by limited sensors and imaging conditions. It is therefore expected that more objective and reliable results will be obtained if continuous research is conducted by accumulating various experimental data in related fields in the future.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.381-391
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• 2022

This paper is intended to find one of the prevailing deep learning models that are a type of AI (Artificial Intelligence) that helps rapidly detect damaged buildings where disasters occur. The models selected are SSD-512, RetinaNet, and YOLOv3 which are widely used in object detection in recent years. These models are based on one-stage detector networks that are suitable for rapid object detection. These are often used for object detection due to their advantages in structure and high speed but not for damaged building detection in disaster management. In this study, we first trained each of the algorithms on xBD dataset that provides the post-disaster imagery with damage classification labels. Next, the three models are quantitatively evaluated with the mAP(mean Average Precision) and the FPS (Frames Per Second). The mAP of YOLOv3 is recorded at 34.39%, and the FPS reached 46. The mAP of RetinaNet recorded 36.06%, which is 1.67% higher than YOLOv3, but the FPS is one-third of YOLOv3. SSD-512 received significantly lower values than the results of YOLOv3 on two quantitative indicators. In a disaster situation, a rapid and precise investigation of damaged buildings is essential for effective disaster response. Accordingly, it is expected that the results obtained through this study can be effectively used for the rapid response in disaster management.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.765-779
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• 2022

This study evaluates the accuracy in identifying the burned area in South Korea using multi-temporal data from Sentinel-2 MSI and Landsat 8/9 OLI. Spectral indices such as the Difference Normalized Burn Ratio (dNBR), Relative Difference Normalized Burn Ratio (RdNBR), and Burned Area Index (BAI) were used to identify the burned area in the March 2022 forest fire in Uljin. Based on the results of six indices, the accuracy to detect the burned area was assessed for four satellites using Sentinel-2 and Landsat 8/9, respectively. Sentinel-2 and Landsat 8/9 produce images every 16 and 10 days, respectively, although it is difficult to acquire clear images due to clouds. Furthermore, using images taken before and after a forest fire to examine the burned area results in a rapid shift because vegetation growth in South Korea began in April, making it difficult to detect. Because Sentinel-2 and Landsat 8/9 images from February to May are based on the same date, this study is able to compare the indices with a relatively high detection accuracy and gets over the temporal resolution limitation. The results of this study are expected to be applied in the development of new indices to detect burned areas and indices that are optimized to detect South Korean forest fires.

• Journal of the Korean GEO-environmental Society
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• v.23 no.6
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• pp.19-23
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• 2022

For the substantiation and verification of studies related to the construction of a levee using riverbed soil, real-scale levee construction and experimental studies are essential. One of the most important factors in the experimental study is the reproducibility of the multiple levees with the same initial conditions. Quantitative analysis of the reproducibility should be presented. In this study, a number of physical properties (specific gravity test, sieving test, liquid-plastic limit test, compaction test, on-site Density test) for multiple embankments built with fine-grained bed soil was obtained. The collected data then used to obtain the possibility of reproducing levee through statistical analysis to suggest a process of indicating a numeric initial condition of the real-scale test. As a result of statistical analysis to verify the aforementioned process, it was confirmed that it was possible to quantitatively evaluate the reproducibility of the construction under the same conditions of embankments. This is expected to be a basic data for a full-scale embankment test using riverbed soil including other soil based real-scale tests.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.29-35
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• 2022

UV rays have beneficial or harmful effects on the human body depending on the degree of exposure. An accurate UV information is required for proper exposure to UV rays per individual. The UV rays' information is provided by the Korea Meteorological Administration as one component of daily weather information in Korea. However, it does not provide an accurate UVI at the user's location based on the region's Ultraviolet index. Some operate measuring instrument to obtain an accurate UVI, but it would be costly and inconvenient. Studies which assumed the UVI through environmental factors such as solar radiation and amount of cloud have been introduced, but those studies also could not provide service to individual. Therefore, this paper proposes a deep learning model to calculate UVI using solar object information and sunlight characteristics to provide an accurate UVI at individual location. After selecting the factors, which were considered as highly correlated with UVI such as location and size and illuminance of sun and which were obtained through the analysis of sky images and solar characteristics data, a data set for DNN model was constructed. A DNN model that calculates the UVI was finally realized by entering the solar object information and sunlight characteristics extracted through Mask R-CNN. In consideration of the domestic UVI recommendation standards, it was possible to accurately calculate UVI within the range of MAE 0.26 compared to the standard equipment in the performance evaluation for days with UVI above and below 8.