• Conservation Science in Museum
• /
• v.24
• /
• pp.55-74
• /
• 2020

The Sacred Bell of Great King Seongdeok is required digital precision recording of conservation conditions because of corrosion and partial abrasion of its patterns and inscriptions. Therefore, this study performed digital documentation of the bell using four types of scanning and unmanned aerial vehicle (UAV) photogrammetry technologies, and performed the various shape analyses through image processing. The modeling results of terrestrial laser scanning and UAV photogrammetry were merged and utilized as basic material for monitoring earthquake-induced structural deformation because these techniques can construct mutual spatial relationships between the bell and its tower. Additionally, precision scanning at a resolution four to nine times higher than that of the previous study provided highly valuable information, making it possible to visualize the patterns and inscriptions of the bell. Moreover, they are well-suited as basic data for identifying surface conservation conditions. To actively apply three-dimensional scanning results to the conservation of the original bell, the time and position of any changes in shape need to be established by further scans in the short-term. If no change in shape is detected by short-term monitoring, the monitoring should continue in medium- and long-term intervals.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.3
• /
• pp.133-139
• /
• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.25 no.1
• /
• pp.16-27
• /
• 2022

Due to climate change, temperature is rising worldwide. Since rapid growth has been achieved focused on cities, South Korea is experiencing serious environmental problems such as heat island and air pollution in urban areas. To solve this problem, the central and each local government are actively promoting tree planting campaigns. This study quantitatively calculated changes in green areas and vegetation of Gumi by the tree planting campaign, and analyzed the temperature changes accordingly. For the target area, the green area, vegetation index, and ground temperature were calculated for 4 different time periods using the given Landsat satellite images. As a result of the study, the green area of was increased by 7.24km² and 4.93km² for two regions, respectively. Accordingly, the vegetation index increased by 0.14 to 0.16, and the temperature decreased by 0.8 to 1.2℃. The Tree planting campaign not only plays a role in lowering the temperature of the city but also does various roles such as air purification, carbon absorption, and providing green rest areas to citizens. Therefore the campaign should be carried out continuously.

• Korean Journal of Environment and Ecology
• /
• v.35 no.6
• /
• pp.659-668
• /
• 2021

The purpose of this study is to propose a ventilation corridor management plan to improve the thermal environment for Busan Metropolitan City. To this end, the characteristics of hot and cool spots in Busan were identified by conducting spatial statistical analysis, and thermal image data from Landsat-7 satellites and major ventilation corridors were analyzed through WRF meteorological simulation. The results showed the areas requiring thermal environment improvement among hot spot areas were Busanjin-gu, Dongnae-gu, industrial areas in Yeonje-gu and Sasang-gu, and Busan Port piers in large-scale facilities. The main ventilation corridor was identified as Geumjeongsan Mountain-Baekyangsan Mountain-Gudeoksan Mountain Valley. Based on the results, the ventilation corridor management strategy is suggested as follows. Industrial facilities and the Busan Port area are factors that increase the air temperature and worsen the thermal environment of the surrounding area. Therefore, urban and architectural plans are required to reduce the facility's temperature and consider the ventilation corridor. Areas requiring ventilation corridor management were Mandeok-dong and Sajik-dong, and they should be managed to prevent further damage to the forests. Since large-scale, high-rise apartment complexes in areas adjacent to forests interfere with the flow of cold and fresh air generated by forests, the construction of high-rise apartment complexes near Geumjeongsan Mountain with the new redevelopment of Type 3 general residential area should be avoided. It is expected that the results of this study can be used as basic data for urban planning and environmental planning in response to climate change in Busan Metropolitan City.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.40 no.3
• /
• pp.239-247
• /
• 2022

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1385-1394
• /
• 2022

This study was to analyze the change in albedo by level-2 land cover map for 20 years(2002-2021) using MODerate resolution Imaging Spectroradiometer (MODIS) data. Also, the difference from the MODIS data was analyzed using the 10-year (2012-2021) data of Visible Infrared Imaging Radiometer Suite (VIIRS). For the albedo data of MODIS and VIIRS, daily albedo data, MCD43A3 and VNP43IA, of 500 m spatial resolution of sinusoidal tile grid produced by Bidirectional Reflectance Distribution Function (BRDF) model were prepared for the South Korea range. Reprojection was performed using the code written based on Python 3.9, and the nearest neighbor was applied as the resampling method. White sky albedo and black sky albedo of shortwave were used for analysis. As a result of 20-year albedo analysis using MODIS data, the albedo tends to rise in all land use. Compared to the 2000s (2002-2011), the average albedo of the 2010s (2012-2021) showed the most significant increase of 0.0027 in the forest area, followed by the grass increase of 0.0024. As a result of comparing the albedo of VIIRS and MODIS, it was found that the albedo of VIIRS was larger from 0.001 to 0.1, which was considered to be due to differences in the surface reflectivity according to the time of image capture and sensor characteristics.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1219-1230
• /
• 2022

Multi-spectral drones in agricultural observation require quantitative and reliable data based on physical quantities such as radiance or reflectance in crop yield analysis. In the case of remote sensing data for crop monitoring, images taken in the same area over time-series are required. In particular, biophysical data such as leaf area index or chlorophyll are analyzed through time-series data under the same reference, it can be directly analyzed. So, comparable reflectance data are required. Orthoimagery using drone images, the entire image pixel values are distorted or there is a difference in pixel values at the junction boundary, which limits accurate physical quantity estimation. In this study, reflectance and vegetation index based on drone images were calculated according to the correction method of drone images for time-series crop monitoring. comparing the drone reflectance and ground measured data for spectral characteristics analysis.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.991-1005
• /
• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_1
• /
• pp.461-469
• /
• 2022

This paper presents a novel training data extraction approach using semi-supervised learning (SSL)-based classification without the analyst intervention for time-series land-cover mapping. The SSL-based approach first performs initial classification using initial training data obtained from past images including land-cover characteristics similar to the image to be classified. Reliable training data from the initial classification result are then extracted from SSL-based iterative classification using classification uncertainty information and class labels of neighboring pixels as constraints. The potential of the SSL-based training data extraction approach was evaluated from a classification experiment using unmanned aerial vehicle images in croplands. The use of new training data automatically extracted by the proposed SSL approach could significantly alleviate the misclassification in the initial classification result. In particular, isolated pixels were substantially reduced by considering spatial contextual information from adjacent pixels. Consequently, the classification accuracy of the proposed approach was similar to that of classification using manually extracted training data. These results indicate that the SSL-based iterative classification presented in this study could be effectively applied to automatically extract reliable training data for time-series land-cover mapping.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.901-912
• /
• 2022

The frequency of disasters is increasing due to global climate change, and unusual heavy rains and rainy seasons are occurring in Korea. Periodic monitoring and rapid detection are important because these weather conditions can lead to drought and flooding, causing secondary damage. Although research using optical images is continuously being conducted to determine the waterbody, there is a limitation in that it is difficult to detect due to the influence of clouds in order to detect floods that accompany heavy rain. Therefore, there is a need for research using synthetic aperture radar (SAR) that can be observed regardless of day or night in all weather. In this study, using Sentinel-1 SAR images that can be collected in near-real time as open data, the UNet model among deep learning algorithms that have recently been used in various fields was applied. In previous studies, waterbody detection studies using SAR images and deep learning algorithms are being conducted, but only a small number of studies have been conducted in Korea. In this study, to determine the applicability of deep learning of SAR images, UNet and the existing algorithm thresholding method were compared, and five indices and Sentinel-2 normalized difference water index (NDWI) were evaluated. As a result of evaluating the accuracy with intersect of union (IoU), it was confirmed that UNet has high accuracy with 0.894 for UNet and 0.699 for threshold method. Through this study, the applicability of deep learning-based SAR images was confirmed, and if high-resolution SAR images and deep learning algorithms are applied, it is expected that periodic and accurate waterbody change detection will be possible in Korea.