• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1027-1036
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• 2020

In this study, a cloud-based processing method using Agisoft Metashape, a commercial software, and Amazon web service, a cloud computing service, is introduced and evaluated to quickly generate high-precision 3D realistic data from large volume UAV images acquired in disaster sites. Compared with on-premises method using a local computer and cloud services provided by Agisoft and Pix4D, the processes of aerial triangulation, 3D point cloud and DSM generation, mesh and texture generation, ortho-mosaic image production recorded similar time duration. The cloud method required uploading and downloading time for large volume data, but it showed a clear advantage that in situ processing was practically possible. In both the on-premises and cloud methods, there is a difference in processing time depending on the performance of the CPU and GPU, but notso much asin a performance benchmark. However, it wasfound that a laptop computer equipped with a low-performance GPU takes too much time to apply to in situ processing.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1693-1705
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• 2023

Rural areas, which account for about 90% of the country's land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents' lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents' lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.

• Economic and Environmental Geology
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• v.53 no.6
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• pp.729-742
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• 2020

In this study, we analyze seabottom conditions and characteristics integrated with topographic data, seafloor mosaic, underwater images and orthophoto(drone) of soft-hard bottom area around the Sib-Ri rock in the northern shore of the East Sea(Gyeongpo Beach, Gangneung). We obtained field survey data around the Sib-Ri rock(about 600 m × 600 m). The Sib-Ri rock is formed by two exposed rocks and surrounding reef. The artificial reef zone made by about 200 ~ 300 structures is shown the western area of the Sib-Ri rock. The underwater rock region is extended from the southwestern area of the exposed the Sib-Ri rock with 9 ~ 11 m depth range. The most broad rocky seabottom area is located in the southwestren area of the Sib-Ri rock with 10 ~ 13 m depth range. The study area were classified into 4 types of seabottom environment based on the analysis of bathymetric data, seafloor mosaics, composition of sediments and images(underwater and drone). The underwater rock zones(Type I) are the most distributed area around the Sib-Ri Rock(about 600 m × 600 m). The soft seabottom area made by sediments layer showed 2 types(Type II: gS(gravelly Sand), Type III: S(Sand)) in the areas between underwater rock zones and western part of the Sib-Ri rock(toward Gyeongpo Beach). The artificial reef zone with a lot of structures is located in the western part of the Sib-Ri rock. Marine algae(about 6 species), Phylum porifera(about 2 species), Phylum echinodermata(about 3 species), Phylum mollusca(about 3 species) and Phylum chordata(about 2 species) are dominant faunal group of underwater image analysis area(about 10 m × 10 m) in the northwestern part of the Sib-Ri rock. The habitat of Phylym mollusca(Lottia dorsuosa, Septifer virgatus) and Phylum arthropoda(Pollicipes mitella, Chthamalus challengeri hoek) appears in the intertidal zone of the Sib-Ri rock. And it is possible to estimate the range and distribution of the habitat based on the integrated study of orthphoto(drone) and bathymetry data. The integrated visualization and mapping techniques using seafloor mosaic images, sediments analysis, underwater images, orthophoto(drone) and topographic data can provide and contribute to figure out the seabottom conditions and characteristics in the shore of the East Sea.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1645-1653
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• 2014

In general, RGB, NIR, and thermal images are used for obtaining geospatial data. Such multiband images are collected via devices mounted on satellites or manned flights, but do not always meet users' expectations, due to issues associated with temporal resolution, costs, spatial resolution, and effects of clouds. We believe high-resolution, multiband images can be obtained at desired time points and intervals, by developing a payload suitable for a low-altitude, auto-piloted UAV. To achieve this, this study first established a low-cost, high-resolution multiband image collection system through developing a sensor and a payload, and collected geo-referencing data, as well as RGB, NIR and thermal images by using the system. We were able to obtain a 0.181m horizontal deviation and 0.203m vertical deviation, after analyzing the positional accuracy of points based on ortho mosaic images using the collected RGB images. Since this meets the required level of spatial accuracy that allows production of maps at a scale of 1:1,000~5,000 and also remote sensing over small areas, we successfully validated that the payload was highly utilizable.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.183-191
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• 2015

If the damage analysis on river-side facilities such as dam, river bank structures and bridges caused by disasters such as typhoon, flood, etc. becomes available, it can be a great help for disaster recovery and decision-making. In this research, We tried to extract a Digital Surface Model (DSM) and analyze the accuracy from Unmanned Air Vehicle (UAV) images on river-side facilities. We tried to apply stereo image-based matching technique, then extracted match results were united with one mosaic DSM. The accuracy was verified compared with a DSM derived from LIDAR data. Overall accuracy was around 3m of absolute and root mean square error. As an analysis result, we confirmed that exterior orientation parameters exerted an influence to DSM accuracy. For more accurate DSM generation, accurate EO parameters are necessary and effective interpolation and post process technique needs to be developed. And the damage analysis simulation with DSM has to be performed in the future.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.705-714
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• 2019

Precision positioning is necessary for various use of high-resolution UAV images. Basically, GCP is used for this purpose, but in case of emergency situations or difficulty in selecting GCPs, the data shall be obtained without GCPs. This study proposed a method of improving positional accuracy for x, y coordinate of UAV based 3 dimensional point cloud data generated without GCPs. Road vector file by the public data (Open Data Portal) was used as reference data for improving location accuracy. The geometric correction of the 2 dimensional ortho-mosaic image was first performed and the transform matrix produced in this process was adopted to apply to the 3 dimensional point cloud data. The straight distance difference of 34.54 m before the correction was reduced to 1.21 m after the correction. By confirming that it is possible to improve the location accuracy of UAV images acquired without GCPs, it is expected to expand the scope of use of 3 dimensional spatial objects generated from point cloud by enabling connection and compatibility with other spatial information data.