• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.4
• /
• pp.223-233
• /
• 2021

Systematic and continuous monitoring and management of the energy consumption of buildings are important for estimating building energy efficiency, and ultimately aim to cope with climate change and establish effective policies for environment, and energy supply and demand policies. Globally, buildings consume 36% of total energy and account for 39% of carbon dioxide emissions. The purpose of this study is to generate three-dimensional thermo-spatial building models with photogrammetric technique using drone TIR (Thermal Infrared) images to measure the temperature emitted from a building, that is essential for the building energy rating system. The aerial triangulation was performed with both optical and TIR images taken from the sensor mounted on the drone, and the accuracy of the models was analyzed. In addition, the thermo-spatial models of temperature distribution of the buildings in three-dimension were visualized. Although shape of the objects 3D building modeling is relatively inaccurate as the spatial and radiometric resolution of the TIR images are lower than that of optical images, TIR imagery could be used effectively to measure the thermal energy of the buildings based on spatial information. This paper could be meaningful to present extension of photogrammetry to various application. The energy consumption could be quantitatively estimated using the temperature emitted from the individual buildings that eventually would be uses as essential information for building energy efficiency rating system.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.30-30
• /
• 2018

Research on mapping levees is an important task for assessing levee stability. The drone imagery acquired in river basins is useful for generating real-time levee maps. This research proposes a robust methodology for mapping levees in river basins using the drone imagery. In the first step, the multiple imagery taken in the test bed was acquired by the drone. Then, the orthorectified image and DEM (Digital Elevation Model) were generated by the photogrammetry and image processing process. Finally, the significant features on levee surfaces such as levee tops, levee lines, levee slopes, eroded areas were detected from the generated DEM and orthorectified image by manual labors and automatic methods. In future research, the automatic procedure for identifying the significant levee features from the drone imagery would be proposed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.6
• /
• pp.441-452
• /
• 2017

Drones are increasingly being used to investigate disaster damage because they can quickly capture images in the air. It is necessary to extract the damaged area by registering the drones and the existing ortho-images in order to investigate the disaster damage. In this process, we might be faced the problem of registering two images with different time and spatial resolution. In order to solve this problem, we propose a new methodology that performs initial image transformation using line pairs extracted from images and association matrix, and final registration of images using linear features to refine the initial transformed result. The applicability of the newly proposed methodology in this study was evaluated through experiments using artifacts and the natural terrain areas. Experimental results showed that the root mean square error of artifacts and the natural terrain was 1.29 pixels and 4.12 pixels, respectively, and relatively high accuracy was obtained in the region with artifacts extracted a lot of linear information.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.4
• /
• pp.271-277
• /
• 2018

Generating an epipolar image which is removed a y-parallax from an original image is an essential technique for creating a 3D stereoscopic model or producing a map. In epipolar image production, there is a method of generating epipolar images by estimating the relative orientation parameters after matching the extracted distinct points in two images and a method of generating epipolar images by using the baseline and rotation angles of the two images after determining the exterior orientation parameters In this study, it was proposed a methodology to generate epipolar images using direction cosine in the exterior orientation parameters of the input images, and a method to use the transformation matrix for easy calculation when converting from the original image to the epipolar image. The applicability of the proposed methodology was evaluated by using images taken from the fixed wing and rotary wing drones. As a result, it was found that epipolar images were generated regardless of the type of drones.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.40 no.3
• /
• pp.147-157
• /
• 2022

Recently, many studies have been conducted to analyze traffic or object recognition that classifies vehicles through artificial intelligence-based prediction models using CCTV (Closed Circuit TeleVision)or drone images. In order to develop an object recognition deep learning model for accurate traffic estimation, systematic data construction is required, and related standardized guidelines are insufficient. In this study, previous studies were analyzed to derive guidelines for establishing artificial intelligence-based training data for traffic estimation using drone images, and business reports or training data for artificial intelligence and quality management guidelines were referenced. The guidelines for data construction are divided into data acquisition, preprocessing, and validation, and guidelines for notice and evaluation index for each item are presented. The guidelines for data construction aims to provide assistance in the development of a robust and generalized artificial intelligence model in analyzing the estimation of road traffic based on drone image artificial intelligence.

• Journal of the Society of Disaster Information
• /
• v.17 no.3
• /
• pp.608-616
• /
• 2021

Purpose: Safety inspections by existing personnel have been limited in evaluation and data securing due to concerns about the safety of technicians or difficulty in accessing them, and are becoming a bigger problem as the number of maintenance targets increases due to the aging of facilities. As drone technology develops, it is possible to ensure the safety of personnel, secure visual data, and diagnose quickly, and use it is increasing as safety inspection of facilities by drones was introduced recently. In order to further enhance utilization, it is considered necessary to base a consideration standard for facility appearance investigation by drones, and in this paper, research was conducted on dams. Method: To calculate the quality, existing domestic safety inspection and drone-related consideration standards were investigated, and procedures related to safety inspection using drones were compared and analyzed to review work procedures and construction types. In addition, empirical data were collected through drone photography and elevation image production for the actual dam. Result: Work types for safety inspection of facilities using drones were derived, and empirical survey results were collected for two dams according to work types. The existing guidelines were applied for the adjustment ratios for each structural type and standard of the facility, and if a meteorological reference point survey was necessary, the unmanned aerial vehicle survey of the construction work standard was applied. Conclusion: The finer the GSD in appearance investigation using drones, the greater the number of photographs taken, and the concept of adjustment cost was applied as a correction to calculate the consideration standard. In addition, it was found that the problem of maximum GSD indicating limitations should be considered in order to maintain the safe distance.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_2
• /
• pp.849-858
• /
• 2023

Investigating disaster sites such as earthquakes and landslides involves significant risks due to potential secondary disasters like facility collapse. In situations where direct access is challenging, there is a need to develop methods for safely acquiring high-precision 3D disaster information using light detection and ranging (LiDAR) equipped drone survey systems. In this study, the feasibility of using drone LiDAR in disaster scenarios was examined, focusing on the collapse accident at Jeongja Bridge in Bundang-gu, Seongnam City, in April 2023. High-density point clouds for the accident bridge were collected, and the bridge's 3D terrain information was reconstructed and compared to the measurement performance of 10 ground control points. The results showed horizontal and vertical root mean square error values of 0.032 m and 0.055 m, respectively. Additionally, when compared to a point cloud generated using ground LiDAR for the same target area, a vertical difference of approximately 0.08 m was observed, but overall shapes showed minimal discrepancies. Moreover, in terms of overall data acquisition and processing time, drone LiDAR was found to be more efficient than ground LiDAR. Therefore, the use of drone LiDAR in disaster sites with significant risks allows for safe and rapid onsite investigations.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.1003-1004
• /
• 2017

본 연구에서는 실내 환경에서 드론의 측위를 위한 마커 인식 및 검출 기술을 소개한다. 기존 실내 측위를 위한 기술인 Global Positioning System이나 Wi-Fi를 이용한 삼각측량 기법은 실내 환경에서 각각의 성질로 인하여 사용하기 어려운 점이 있다. 본 논문에서는 2차원 바코드와 마커 등의 객체를 드론의 카메라를 이용한 실시간 영상 전송을 통하여 검출하여 위치 정보를 획득하는 기술을 소개한다. 실험에서는 드론의 카메라를 통하여 실시간 전송된 영상에서 OpenCV V2.4.10을 통하여 객체를 검출하였고, 카메라와 객체 사이의 거리와 바코드 크기에 따른 2차원 바코드의 검출 여부를 보였으며 15*15cm의 2차원 바코드는 비교적 잘 인식하였으나 비교적 작은 11*11cm의 2차원 바코드는 거리가 멀어질 수록 인식이 힘들어지는 결과를 보였다.

• Geophysics and Geophysical Exploration
• /
• v.23 no.3
• /
• pp.141-148
• /
• 2020

A drone platform built by Korea Institute of Geoscience and Mineral Resources (KIGAM) is introduced. The platform consists of various drone systems developed at KIGAM for photogrammetry, remote exploration, physical exploration, field operation methods, a vehicle-based drone control center, as well as a drone data platform for data storage, sharing, analysis, and visualization of the acquired data. The performance of the drone platform is verified using results obtained with the various systems, which are tested individually and in various combined applications. Finally, the possibility of using the KIGAM drone platform for geological surveys and mineral resource exploration is discussed.

• Tunnel and Underground Space
• /
• v.25 no.5
• /
• pp.462-469
• /
• 2015

This study carried out a topographic survey at a small-scale open-pit limestone mine in Korea (the Daesung MDI Seoggyo office) using a popular rotary-wing unmanned aerial vehicle (UAV, Drone, DJI Phantom2 Vision+). 89 sheets of aerial photos could be obtained as a result of performing an automatic flight for 30 minutes under conditions of 100m altitude and 3m/s speed. A total of 34 million cloud points with X, Y, Z-coordinates was extracted from the aerial photos after data processing for correction and matching, then an orthomosaic image and digital surface model with 5m grid spacing could be generated. A comparison of the X, Y, Z-coordinates of 5 ground control points measured by differential global positioning system and those determined by UAV photogrammetry revealed that the root mean squared errors of X, Y, Z-coordinates were around 10cm. Therefore, it is expected that the popular rotary-wing UAV photogrammetry can be effectively utilized in small-scale open-pit mines as a technology that is able to replace or supplement existing topographic surveying equipments.