• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.161-166
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• 2004

Seoul has experienced a rapid urban expansion over the past three decades. This paper reports an investigation into the application of Landsat imagery for detecting urban growth and assessing its impact on surface temperature in the region. Land cover/use change detection w3s carried out by using Landsat data. The results revealed a notable urban growth in the study area. This urban expansion had raised surface radiant temperature in the urbanized area. The method using remote sensing data based on GIS was found to be effective in monitoring and analysing urban growth and in evaluating urbanization impact on surface temperature.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.827-830
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• 2008

Enterococcus is a fecal indicator bacterium and often used to indicate fecal contamination in the environment. Carbohydrates fermentation patterns of Enterococcus isolates were investigated as a way to differentiate the source of fecal contamination. Total 1826 Enterococcus isolates were obtained from cows, pigs, chickens, ducks, and humans in two geographically different locations. Distributions of carbohydrate fermentation patterns showed discrepancies among sources. This study suggest that the possibility of the use of Enterococcus in microbial source tracking.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.13-14
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• 2023

집중 안전 점검의 대상인 노후 건축물에서 균열은 건물의 안전도를 점검할 수 있는 지표이다. 안전 점검에 드론을 활용하면서 고해상도의 드론 기반 균열 이미지 수집이 가능해졌고, 육안이 아닌 AI기반으로 균열을 탐지, 구획화할 수 있다. 본 연구에서는 주변 사물과 배경에 구애받지 않고 안전 점검이 가능한 구획화 알고리즘을 제안한다. METU와 POC데이터셋을 가공하여 데이터셋을 구축하고, 이를 바탕으로 ResNet50을 통해 균열과 유사한 배경을 분류하였으며, 균열 구획화 모델을 선정하여 DesneNet201-UNet++으로 mIoU 82.27%를 달성하였다. 본 연구는 노후 건축물 안전 점검에 필요한 균열 폭 추정에 도움이 될 것으로 기대된다.

• The Journal of Engineering Geology
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• v.24 no.4
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• pp.525-534
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• 2014

Environmental problems typically occurring in abandoned mine lands (AML) include: contaminated and acidic surface water and groundwater; stockpiled waste rock and mill tailings; and ground subsidences due to mining operations. This study examines the effectiveness of various geophysical techniques for mapping potential hazard and contaminated zones. Four AML sites with sedimentation contamination problems, acid mine drainage (AMD) channels, ground subsidence, manmade liner leakage, and buried mine tailings, were selected to examine the applicability of various geophysical methods to the identification of the different types of mine hazards. Geophysical results were correlated to borehole data (core samples, well logs, tomographic profiles, etc.) and water sample data (pH, electrical conductivity (EC), and heavy metal contents). Zones of low electrical resistivity (ER) corresponded to areas contaminated by heavy metals, especially contamination by Cu, Pb, and Zn. The main pathways of AMD leachate were successfully mapped using ER methods (low anomaly peaks), self-potential (SP) curves (negative peaks), and ground penetrating radar (GPR) at shallow penetration depths. Mine cavities were well located based on composite interpretations of ER, seismic tomography, and well-log records; mine cavity locations were also observed in drill core data and using borehole image processing systems (BIPS). Damaged zones in buried manmade liners (used to block descending leachate) were precisely detected by ER mapping, and buried rock waste and tailings piles were characterized by low-velocity zones in seismic refraction data and high-resistivity zones in the ER data.

• Geophysics and Geophysical Exploration
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• v.1 no.3
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• pp.176-182
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• 1998

The borehole radar reflection survey can image the underground structure with high resolution, however, we cannot get any information on the orientation of the reflectors with dipole antenna alone. The direction-finding antenna system is commonly used to give the solution to the problem. However, the interpretation of the data from direction- finding antenna may be time-consuming, and sometimes have ambiguities in the sense of precise determination of the azimuth. To solve the problem, we developed the automatic azimuth finding scheme of reflectors in borehole radar reflection data using direction-finding antenna. The algorithm is based on finding the azimuthal angle possibly showing the maximum reflection amplitude in the least-squared error sense. The developed algorithm was applied to the field data acquired in quarry mine. It was possible to locate nearly all of the reflectors in three dimensional fashion, which coincide with the known geological structures and man-made discontinuities.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.91-98
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• 2021

Today's information and communication technology is rapidly developing, the security of IT infrastructure is becoming more important, and at the same time, cyber attacks of various forms are becoming more advanced and sophisticated like intelligent persistent attacks (Advanced Persistent Threat). Early defense or prediction of increasingly sophisticated cyber attacks is extremely important, and in many cases, the analysis of network-based intrusion detection systems (NIDS) related data alone cannot prevent rapidly changing cyber attacks. Therefore, we are currently using data generated by intrusion detection systems to protect against cyber attacks described above through Host-based Intrusion Detection System (HIDS) data analysis. In this paper, we conducted a comparative study on machine learning algorithms using LID-DS (Leipzig Intrusion Detection-Data Set) host-based intrusion detection data including thread information, metadata, and buffer data missing from previously used data sets. The algorithms used were Decision Tree, Naive Bayes, MLP (Multi-Layer Perceptron), Logistic Regression, LSTM (Long Short-Term Memory model), and RNN (Recurrent Neural Network). Accuracy, accuracy, recall, F1-Score indicators and error rates were measured for evaluation. As a result, the LSTM algorithm had the highest accuracy.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.33-43
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• 2024

This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non-point source pollution. Utilizing high-resolution imagery acquired through Unmanned Aerial Vehicles(UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non-point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non-point source pollution management strategies, and contributing to the safeguarding of aquatic environments.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.2
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• pp.69-78
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• 2011

Rice is one of the world's staple foods. Paddy rice fields have unique biophysical characteristics that the rice is grown on flooded soils unlike other crops. Information on the spatial distribution of paddy fields and the timing of irrigation are of importance to determine hydrological balance and efficiency of water resource management. In this paper, we detected the timing of irrigation and spatial distribution of paddy fields using the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Aqua satellite. The timing of irrigation was detected by the combined use of MODIS-based vegetation index and Land Surface Water Index (LSWI). The detected timing of irrigation showed good agreement with field observations from two flux sites in Korea and Japan. Based on the irrigation detection, a land cover map of paddy fields was generated with subsidiary information on seasonal patterns of MODIS enhanced vegetation index (EVI). When the MODISbased paddy field map was compared with a land cover map from the Ministry of Environment, Korea, it overestimated the regions with large paddies but underestimated those with small and fragmented paddies. Potential reasons for such spatial discrepancies may be attributed to coarse pixel resolution (500 m) of MODIS images, uncertainty in parameterization of threshold values for discarding forest and water pixels, and the application of LSWI threshold value developed for paddy fields in China. Nevertheless, this study showed that an improved utilization of seasonal patterns of MODIS vegetation and water-related indices could be applied in water resource management and enhanced estimation of evapotranspiration from paddy fields.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1373-1387
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• 2021

Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy conditions. Therefore, in this study, Sentinel-1 Synthetic Aperture Radar (SAR) data from Europe Space Agency, which can be collected in all weather conditions, were used to identify forest fire damaged area based on a series of processes including Principal Component Analysis (PCA) and K-means clustering. Four forest fire cases, which occurred in Gangneung·Donghae and Goseong·Sokcho in Gangwon-do of South Korea and two areas in North Korea on April 4, 2019, were examined. The estimated burned areas were evaluated using fire reference data provided by the National Institute of Forest Science (NIFOS) for two forest fire cases in South Korea, and differenced normalized burn ratio (dNBR) for all four cases. The average accuracy using the NIFOS reference data was 86% for the Gangneung·Donghae and Goseong·Sokcho fires. Evaluation using dNBR showed an average accuracy of 84% for all four forest fire cases. It was also confirmed that the stronger the burned intensity, the higher detection the accuracy, and vice versa. Given the advantage of SAR remote sensing, the proposed statistical processing and K-means clustering-based approach can be used to quickly identify forest fire damaged area across the Korean Peninsula, where a cloud cover rate is high and small-scale forest fires frequently occur.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.639-645
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• 2016

Yeongjong-do is seventh largest island in the west coast of Korea which is 4.8 km away in the direction of south-west from Incheon. The mudflat area around the Yeongjong-do has variable dimension according to tide level. In addition, Yeongjong-do is important area with high environmental value as wintering sites for migratory birds. The mudflat of Yeongjong-do is also meaningful region because it is used as place of education and tourist attraction. But, there are increasing concerns about preservation of mudflat area caused by artificial development such as land reclamation project and Incheon airport construction. In this paper, mudflat area was detected using Landsat 7 ETM+ images that United States Geological Survey (USGS) is providing the data in 16 days period. The false color composite was made from band 7, 5, and 3 that could dividing boundary between water and land for the purpose of appearance of boundary line in mudflat region. This area was calculated using results of classification based on false color composite images and was digitized through repetitive algorithm during research of period. Therefore, the change of northeastern area in Yeongjong-do was detected according to tide level during 16 years from 2000 to 2015 on the basis of providing period at tide station. This paper will expect as indicator for range of area in same tide level prior to the start of the research for preservation of mudflat. It will be also scientific grounds about change of mudflat area caused by artificial development.