• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.76-76
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• 2019

최근 기후변화로 인해 퇴적 및 세굴이 심화되었고, 4대강 사업으로 인해 급격한 하천 지형 변화가 발생하였다. 특히, 4대강 사업 이후 하상변동 모니터링에 대한 지속적인 수요가 증가하고 있다. 이에 따라 개정된 하천법에 따르면 하상변동조사를 기본 10년 주기로 정기적으로 실시하되, 퇴적 및 세굴 발생 구간에 대해서는 기본 2년을 기준으로 하상변동이 큰 곳에 대해서는 1년 주기로, 하상변동이 작은 곳에 대해서는 5년 주기로 실시해야 한다. 하지만 기술 및 예산의 한계로 인해 하상변동조사의 경우 현장 유사량 및 하상토 입도 측정과 유량-총유사량 관계식을 활용하여 모델링을 통해 하상변동을 예측하고 있는 실정이다. 하상변동은 기본적으로 하천 수심을 기본으로 하고 있으며, 사람이 직접 투입하여 임의의 지점에 대한 수심 계측을 실시하고 있다. 하지만 직접 수심 계측의 경우 낮은 자료의 밀도로 인해 많은 인력과 예산, 시간이 소요되며 무엇보다도 관측 대상인 물이라는 작업환경에서는 인명피해가 발생할 수 있는 문제점을 가지고 있다. 최근에는 이러한 한계를 극복하기 위해 초음파 센서가 탑재된 이동식 보트를 활용하여 경로형 수심 계측을 실시하고 있으나, 초음파 센서가 가지는 기기적 한계로 인해 약 50cm 이하에 대한 수심은 측정이 어려운 실정이다. 특히, 국내 하천의 경우는 홍수기를 제외하면 수심이 얕기 때문에 얕은 수심에 대한 자료 확보가 어려워 공간적 정밀도 확보가 어려운 실정이다. 따라서 기존의 하천계측의 패러다임을 지점, 선형 계측이 아닌 면 측량을 실시를 통해 높은 밀도의 자료를 확보해야 한다. 이러한 측량이 가능한 기술로 하천원격탐사가 대안으로 제시되고 있다. 하천원격탐사는 직접 접촉하지 않고, 대상체의 광학적 특성을 통해 물리적 특성을 파악하는 기술로서 적은 시간에 높은 밀도의 자료의 확보와 저예산의 고효율 방법으로 알려져 있다. 본 연구에서는 기존의 하천원격탐사에서 활용한 고비용, 저해상도의 시공간 스케일에 해당하는 위성 및 유인항공기가 아닌 하천의 흐름방향으로 비행이 가능하고 상대적으로 저비용, 고해상도의 시공간 스케일의 측정이 가능한 드론을 활용하여 하천원격탐사를 수행하는 방법에 대해 논하고자 한다. 특히, 기술적 한계가 존재하는 청색, 녹색, 적색에 해당하는 RGB 영상을 활용한 하천원격탐사를 극복하기 위한 대안으로 초분광 영상을 수집하고 활용하는 방법을 제시하고자 한다.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.2
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• pp.79-86
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• 2020

In order to accommodate the increase in urban population due to government-led national planning and economic growth, many buildings such as houses and business building were supplied. Although the building law was revised and managed to manage the supplied buildings, for the sake of economic benefit, there have been buildings that are enlarged or reconstructed without declaring building permits. In order to manage these buildings, on-site surveys were conducted. but it has many personnel consumption. To solve this problem, a method of using a satellite image and a manned aircraft is utilized, but it is diseconomical and a renewal cycle is long. In addition, it is not utilized to the height, and although it is judged by the shading of the building, it has limitations that it must be calculated individually. In this study, height of the building was calculated by using the unmanned aerial vehicle with low personnel consumption, and the accuracy was verified by comparison with the building register and measured value. In this study, spatial information was constructed using a fast unmanned aerial vehicle with low manpower consumption and the building height was calculated based on this. The accuracy by comparing the calculated building height with the building register and the actual measurement.

• Journal of Aerospace System Engineering
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• v.15 no.1
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• pp.102-110
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• 2021

Recently, multi-copters equipped with various collision avoidance sensors have been introduced to improve flight stability. LiDAR is used to recognize a three-dimensional position. Multiple cameras and real-time SLAM technology are also used to calculate the relative position to obstacles. A three-dimensional depth sensor with a small process and camera is also used. In this study, a small collision-avoidance multi-copter system capable of in-door flight was developed as a platform for the development of collision avoidance software technology. The multi-copter system was equipped with LiDAR, 3D depth sensor, and small image processing board. Object recognition and collision avoidance functions based on the YOLO algorithm were verified through flight tests. This paper deals with recent trends in drone collision avoidance technology, system design/manufacturing process, and flight test results.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.6
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• pp.25-33
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• 2022

In this study, a monitoring method using high-resolution images acquired by unmanned aerial vehicles and deep learning algorithms was proposed for the management of the Sinduri coastal sand dunes. Class classification was done using U-net, a semantic division method. The classification target classified 3 types of sand dune vegetation into 4 classes, and the model was trained and tested with a total of 320 training images and 48 test images. Ignored label was applied to improve the performance of the model, and then evaluated by applying two loss functions, CE Loss and BCE Loss. As a result of the evaluation, when CE Loss was applied, the value of mIoU for each class was the highest, but it can be judged that the performance of BCE Loss is better considering the time efficiency consumed in learning. It is meaningful as a pilot application of unmanned aerial vehicles and deep learning as a method to monitor and manage sand dune vegetation. The possibility of using the deep learning image analysis technology to monitor sand dune vegetation has been confirmed, and it is expected that the proposed method can be used not only in sand dune vegetation but also in various fields such as forests and grasslands.

• The Journal of Engineering Geology
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• v.33 no.1
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• pp.27-37
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• 2023

Assessing landcreep requires long-term monitoring, because cracks and steps develop over long periods. However, long-term monitoring using wire extensometers and inclinometers is inefficient in terms of cost and management. Therefore, this study selected an area with active landcreep and evaluated the feasibility of monitoring it using imagesing from terrestrial LiDAR and drones. The results were compared with minute-by-minute data measured in the field using a wire extensometer. The comparison identified subtle differences in the accuracy of the two sets of results, but monitoring using terrestrial LiDAR and drones did generate values similar to the wire extensometer. This demonstrates the potential of basic monitoring using terrestrial LiDAR and drones, although minute-byminute field measurements are required for analyzing and predicting landcreep. In the future, precise monitoring using images will be feasible after verifying image analysis at various levels and accumulating data considering climate and accuracy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.160-167
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• 2023

This study focuses on the development of a Last-Mile delivery service using unmanned vehicles to deliver goods directly to the end consumer utilizing drones to perform autonomous delivery missions and an image-based precision landing algorithm for handoff to a robot in an intermediate facility. As the logistics market continues to grow rapidly, parcel volumes increase exponentially each year. However, due to low delivery fees, the workload of delivery personnel is increasing, resulting in a decrease in the quality of delivery services. To address this issue, the research team conducted a study on a Last-Mile delivery service using unmanned vehicles and conducted research on the necessary technologies for drone-based goods transportation in this paper. The flight scenario begins with the drone carrying the goods from a pickup location to the rooftop of a building where the final delivery destination is located. There is a handoff facility on the rooftop of the building, and a marker on the roof must be accurately landed upon. The mission is complete once the goods are delivered and the drone returns to its original location. The research team developed a mission planning algorithm to perform the above scenario automatically and constructed an algorithm to recognize the marker through a camera sensor and achieve a precision landing. The performance of the developed system has been verified through multiple trial operations within ETRI.

• Computers and Concrete
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• v.33 no.4
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• pp.425-443
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• 2024

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.111-126
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• 2017

Small-scale UAS(Fusion technique of Unmanned Aerial Vehicles platform and Sensors, 'sUAS') opens various new applications in construction fields and so becoming progressively common due to the considerable potentials in terms of accuracy, costs and abilities. The purpose of this study is that the investigation of the validation on the utilization of sUAS for earth stockpile volume calculations on sites. For this, generate 3D models(DSM) with sUAS aerial images on an cone shaped soil stockpile approximately $270m{\times}300m{\times}20m$, which located at Baegot Life Park in Siheung-si, compared stockpile volume estimates produced by sUAS image analysis, against volume estimates obtained by GNSS Network-RTK ground surveying method which selected as the criteria of earth stockpile volume. The result through comparison and examination show(demonstrate) that there was under 2% difference between the volume calculated with the GNSS Network RTK data and the sUAV data, especially sUAS imaged-based volume estimate of a stockpile can be greatly simplified, done quickly, and very cost effective over conventional terrestrial survey methods. Therefore, with consideration of various plan to assess the height of vegetation, sUAS image-based application expected very useful both volume estimate and 3D geospatial information extraction in small and medium-sized sites.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.587-593
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• 2019

The field of geospatial information is rapidly changing due to the development of sensor and data processing technology that can acquire location information. And demand is increasing in various related industries and social activities. The construction and utilization of three dimensional geospatial information that is easy to understand and easy to understand can be an essential element to improve the quality and reliability of related services. In recent years, 3D laser scanners are widely used as 3D geospatial information construction technology. However, 3D laser scanners may cause shadow areas where data acquisition is not possible when objects are large in size or complex in shape. In this study, 3D model of an object has been created by acquiring oblique images using an unmanned aerial vehicle and processing the data. The study area was selected, oblique images were acquired using an unmanned aerial vehicle, and point cloud type 3D model with 0.02 m spacing was created through data processing. The accuracy of the 3D model was 0.19m and the average was 0.11m. In the future, if accuracy is evaluated according to shooting and data processing methods, and 3D model construction and accuracy evaluation and analysis according to camera types are performed, the accuracy of the 3D model will be improved. In the point cloud type 3D model, Cross section generation, drawing of objects, and so on, it is possible to improve work efficiency of spatial information service and related work.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.367-378
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• 2021

Field compost is a representative non-point pollution source for livestock. If the field compost flows into the water system due to rainfall, nutrients such as phosphorus and nitrogen contained in the field compost can adversely affect the water quality of the river. In this paper, we propose a method for detecting field compost using unmanned aerial vehicle images and deep learning-based semantic segmentation. Based on 39 ortho images acquired in the study area, about 30,000 data were obtained through data augmentation. Then, the accuracy was evaluated by applying the semantic segmentation algorithm developed based on U-net and the filtering technique of Open CV. As a result of the accuracy evaluation, the pixel accuracy was 99.97%, the precision was 83.80%, the recall rate was 60.95%, and the F1-Score was 70.57%. The low recall compared to precision is due to the underestimation of compost pixels when there is a small proportion of compost pixels at the edges of the image. After, It seems that accuracy can be improved by combining additional data sets with additional bands other than the RGB band.