In this study, an analysis were conducted to utilize the thermal infrared image using drone to present the temperature correction method of thermal infrared image and the thermal environment by the type of land cladding. The analysis was applied to the temperature correction of the thermal infrared image and total eight thermal infrared images were produced based on the land surface temperature. The thermal infrared image compared accuracy through RMSE calculation. Based on the result of RMSE, the thermal infrared image corrected by the land surface temperature was relatively accurate and contained at 2.26 to 3.58. According to the results, it is expected that the aggregation and waters will perform the functions of the green park sufficiently to improve the thermal comfort and improve the microclimate stability using the thermal infrared image and the reclassified land cover map. The results of this study obtained by Drone and the usability of the drone thermal infrared image in the detection of the thermal environment. Finally, it is expected to contribute to the improvement and management of the thermal environment in the city by being used as a basic data for the improvement and management policy of the thermal environment. Moreover, the macro view is expected to contribute to the mitigation of urban temperature reduction and heat island.

Ecological disturbance plants distributed throughout the country are causing a lot of damage to us directly or indirectly in terms of ecology, economy and health. These plants are not easy to manage and remove because they have a strong fertility, and it is very difficult to express them quantitatively. In this study, drone hyperspectral sensor data and Field spectroradiometer were acquired around the experimental area. In order to secure the quality accuracy of the drone hyperspectral image, GPS survey was performed, and a location accuracy of about 17cm was secured. Spectroscopic libraries were constructed for 7 kinds of plants in the experimental area using a Field spectroradiometer, and drone hyperspectral sensors were acquired in August and October, respectively. Spectral data for each plant were calculated from the acquired hyperspectral data, and spectral angles of 0.08 to 0.36 were derived. In most cases, good values of less than 0.5 were obtained, and Ambrosia trifida and Lactuca scariola, which are common in the experimental area, were extracted. As a result, it was found that about 29.6% of Ambrosia trifida and 31.5% of Lactuca scariola spread in October than in August. In the future, it is expected that better results can be obtained for the detection of ecosystem distribution plants if standardized indicators are calculated by constructing a precise spectral angle standard library based on more data.

In this study, carbon dioxide concentration and air temperature at different elevations were observed and analyzed in the upper atmosphere of mud flat and reed beds at low tide in Suncheon Bay. The carbon dioxide concentration and air temperature sensors were mounted on the drone, and the carbon dioxide concentration and air temperature by altitude (5 m, 10 m, 20 m, 40 m) at five points in the tidal flat and reed beds were observed in the morning and afternoon. The carbon dioxide concentrations in the upper atmosphere of the tidal flat ranged from 453.0 to 460.2 ppm in the morning and 441.6 to 449.7 ppm in the afternoon. The carbon dioxide concentrations in the upper atmosphere of the reed beds ranged from 448.9 to 452.4 ppm in the morning and 446.0 to 454.4 ppm in the afternoon. The carbon dioxide concentrations in the upper atmosphere of the tidal flat was higher in the morning than in the afternoon, and the carbon dioxide concentration decreased as the altitude increased. The carbon dioxide concentration in the upper atmosphere of the reed beds was similar in the morning and afternoon at all altitudes, and the carbon dioxide concentration decreased as the altitude increased. The correlation coefficient between carbon dioxide concentration and air temperature observed in the tidal flat in the morning was -0.54 ~ -0.77, and the correlation coefficient between carbon dioxide concentration and air temperature observed in the afternoon was 0.56 ~ 0.80. The correlation coefficient between carbon dioxide concentration and temperature observed in the morning in the reed field was low, below 0.3, and the correlation coefficient between carbon dioxide concentration and air temperature observed in the afternoon was 0.35 ~ 0.77. In the upper atmosphere of the tidal flats and reed beds, the linear function was suitable for the change of carbon dioxide concentration as a air temperature, and the coefficient of determination of the estimated linear function was higher in the afternoon than in the morning. Through this study, it was confirmed that the carbon dioxide concentration in the upper atmosphere of the tidal flat and the reed beds was different, and the increase rate of carbon dioxide concentration in the upper atmosphere of the tidal flat and the reed beds was higher in the afternoon than in the morning.

최근 비탈면의 다양한 붕괴사고, 지반변형 등으로 인하여 비탈면 유지관리에 대한 관심이 증대되고 있는 실정이다. 일반적으로 비탈면에 대한 점검자의 접근성이 양호한 경우는 조치 및 대응이 적기에 취해질 수 있으나, 점검자의 접근성이 곤란한 경우는 장기적인 관점에서 비탈면의 유지관리가 용이하지 않은 것이 현실이다. 본 연구는 점검자의 접근성이 양호하지 않은 비탈면에 대해서 무인체(드론)을 활용하여 비탈면 지반거동의 경시변화를 관찰하고, 이들 결과를 통해서 전반적인 지반 안정성을 사전에 파악하고자 하는 유지관리 사례를 소개하고자 한다.

This Study reviewed the case of Gyeonggi-do province which recently induced Drone to pavement management system in order to research a plan to utilize recent technology Drone to pavement management system which is being operated depending on vehicles and manpower for 30 years. After filming video on local roads managed by Gyeonggi-do province by using Drone directly and created point cloud having three dimensional coordinate and color value through program analysis. Furthermore, this research showed that when we found similar color to point cloud at pavement crack part and applied color, crack part was detected.

We developed a small sensor observation system (SSOS) at a relatively low cost to observe the atmospheric boundary layer. The accuracy of the SSOS sensor was compared with that of the automatic weather system (AWS) and meteorological tower at the Korea Meteorological Administration (KMA). Comparisons between SSOS sensors and KMA sensors were carried out by dividing into ground and lower atmosphere. As a result of comparing the raw data of the SSOS sensor with the raw data of AWS and the observation tower by applying the root-mean-square-error to the error, the corresponding values were within the error tolerance range (KMA meteorological reference point: humidity ± 5%, atmospheric pressure ± 0.5 hPa, temperature ± 0.5℃. In the case of humidity, even if the altitude changed, it tends to be underestimated. In the case of temperature, when the altitude rose to 40 m above the ground, the value changed from underestimation to overestimation. However, it can be confirmed that the errors are within the KMA’s permissible range after correction.

This study is to develop a bridge inspection technology through convergence of advanced technologies such as drone technology and hybrid image scanning technology. Through this study, a UAV(Unmanned Aerial Vehicle) user guideline framework for bridge condition evaluation is proposed. It is presented for writing UAV user guideline that is applicate a field of a bridge inspection using this proposal framework in this study.

암반사면을 안전하고 효과적으로 해석하기 위해서 암반의 역학적 특성을 면밀하게 조사해야 한다. 하지만 클리노미터를 사용한 절리조사의 한계점으로 인해 이를 보완한 새로운 측정법의 연구가 필요하다. 본 연구에서는 절리방향의 특성을 분석하기 위해 절리의 방향성을 현장에 적용할 수 있는 절리조사 측정장비를 개발하였다. 개발된 측정장비는 해석 소프트웨어와 하드웨어로 구분된다. 하드웨어는 암반 절리 방향성을 측정하는 측정모듈, 측정자료를 전송하는 전송모듈로 구성되었다. 소프트웨어는 측정모듈을 통해 얻은 데이터로부터 절리의 방향성을 분석하기 위해 개발하였으며 Drone Joint Orientation Survey Measurement로 명명하였다. 개발된 측정장비는 접근이 어려운 지역 등 조사자가 측정할 수 없는 경우에 현장적용성이 양호하며 절리의 방향성에 대한 실내시험결과를 효과적으로 분석할 수 있었다.

To fly a drone or unmanned aerial vechicle(UAV) safely, its pilot needs to maintain high situation awareness of its flight space. One of the important ways to improve the flight space awareness is to integrate both the global and the local navigation map a drone provides. However, the drone pilot often has to use the inconsistent reference frames or perspectives between the two maps. In specific, the global navigation map tends to display space information in the third-person perspective, whereas the local map tends to use the first-person perspective through the drone camera. This inconsistent perspective problem makes the pilot use mental rotation to align the different perspectives. In addition, integrating different dimensionalities (2D vs. 3D) of the two maps may aggravate the pilot’s cognitive load of mental rotation. Therefore, this study aims to investigate the relation between perspective difference (0°, 90°, 180°, 270°) and the map dimensionality matches (3D-3D vs. 3D-2D) to improve the way of integrating the two maps. The results show that the pilot’s flight space awareness improves when the perspective differences are smaller and also when the dimensionalities between the two maps are matched.

본 논문에서는 효과적인 레이싱 드론 조종 훈련을 위한 VR 콘텐츠의 기획 및 제작에 대해 다루고자 한다. 실제 환경에서의 직접적인 레이싱 드론 조종 훈련은 공간적, 경제적 한계점이 분명한데, 이러한 한계점에 대한 해결책으로 VR 기반 드론 잠입액션 게임 <Drone S>의 제작을 통해 제시하고, 효과적인 드론 조종 능력 증진을 강구하고자 한다. 특히 FPV 고글과 HMD 기반 VR의 유사성을 활용하여 실제와 유사한 환경에서의 가상현실 콘텐츠를 기획하였으며, 레이싱 드론과 드론 레이싱에서 사용되는 구조물의 특징을 고려하여 가상 드론과 맵 및 장애물을 디자인하였다. 또한 점진적으로 증가하는 장애물의 난이도를 통해 사용자의 몰입도와 현존감을 높이고자 하였다.

This paper presents the applicability and reliability of the crack detection technique of concrete structures developed based on the use of digital image analysis technologies through on - site tests. The problem of aging of infrastructure is a serious threat to the national and national security and there is a growing interest in the development and application of effective inspection and maintenance techniques for related infrastructure. Therefore, instead of the existing traditional manpower-based infrastructure inspection and maintenance techniques, which involve lots of time and money consumption and reliability of results, research using digital image analysis technology is actively being carried out.

The specification of surface vegetation is essential for simulating actual evapotranspiration of water resources. The availability of land cover maps based on remotely collected data makes the specification of surface vegetation easier. The spatial resolution of hydrologic models rarely matches the spatial scales of the vegetation data needed, and remotely collected vegetation data often are upscaled up to conform to the hydrologic model scale. In this study, the effects of the grid scale of of surface vegetation on the results of actual evapotranspiration were examined. The results show that the coarser resolution causes larger error in relative terms and that a more realistic description of area-averaged vegetation nature and characteristics needs to be considered when calculating actual evapotranspiration.

As drones gain more popularity these days, drone detection becomes more important part of the drone systems for safety, privacy, crime prevention and etc. However, existing drone detection systems are expensive and heavy so that they are only suitable for industrial or military purpose. This paper proposes a novel approach for training Convolutional Neural Networks to detect drones from images that can be used in embedded systems. Unlike previous works that consider the class probability of the image areas where the class object exists, the proposed approach takes account of all areas in the image for robust classification and object detection. Moreover, a novel loss function is proposed for the CNN to learn more effectively from limited amount of training data. The experimental results with various drone images show that the proposed approach performs efficiently in real drone detection scenarios.

Aquatic plants serve the crucial function of helping to balance water reservoir ecosystem, as they filter and remove major minerals required for algal growth such as nitrogen, ammonia, and nitrates. Aquatic plants provide food, shade, and protection for the aquatic biome in and around the reservoir. Thus, it is important to accurately determine the existence and areal extent of the aquatic plants. In the present study drone-based facilities were used for this purpose. In the Muncheon water reservoir, Gyeongbuk, the Normalized Difference Vegetation Index (NDVI) and Surface Algal Bloom Index (SABI) were used to determine the existence status of the aquatic plants. The data so obtained exhibited reasonable accuracy; drone-based facilities can be used in future to identify the areal extent of aquatic plants.

In this study, a drone based structural health monitoring technique is introduced which uses a piezoelectric (PZT) transducer attached to a drone. With the PZT transducer, the electromechanical impedance (EMI) method is modified to be attached and re-attached onto a structure for damage identification. Since one of the possible principle technology is to keep the tube structure safe from damage, the idea introduced in this study opens up new possibilities of monitoring the integrity of the Hyperloop structure.

Drought is a reoccurring worldwide natural hazard that affects not only food production but also economics, health, and infrastructure. Drought monitoring is usually performed with precipitation-based indices without consideration of the actual state and amount of the land surface properties. A drought index based on the actual evapotranspiration can overcome these shortcomings. The severity of a drought can be quantified by making a spatial map. The procedure for estimating actual evapotranspiration is costly and complicated, and requires land surface information. The possibility of utilizing drone-driven remotely sensed data for actual evapotranspiration estimation was analyzed in this study. A drone collected data was used to calculate the normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI). The spatial resolution was 10 m with a grid of 404 x 395. The collected data were applied and parameterized to an actual evapotranspiration estimation. The result shows that drone-based data is useful for estimating actual evapotranspiration and the corresponding drought indices.