• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.6
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• pp.97-110
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• 2018

This study examined the value of the old roads and roadscapes as modern assets. Topographic maps of the two years (1916 and 1919, which were produced by the Japanese Government-General of Korea) and the digital topographic map produced in 2017, were analyzed. The total amount of roads that have survived for the past 100 years are located in 108 places and total 26.32km. After examining the remnants of the roads in YeongDo, the type of scenery experienced along the roads were classified into nine kinds. The place where a sequential scenery experience takes place due to the survival of the past, the experience is based on the transition of historical scenery, not the scenery of the present time. A new model that can preserve, manage and plan this scenery is required. Therefore, we propose a new landscape model that elevates the concept of gaze from a spatial concept to a spatiotemporal concept. Based on this model, we propose a conservation criterion of the landscape viewed on the road as a viewpoint. As a modern asset for the next 100 years of YeongDo, it is necessary to understand and preserve the meaning of the landscape and roadside scenery as a transit landscape network. The remnant of roads from 100 years ago suggests that the scenery on the road was has been maintained, and it is the historical landscape of the YeongDo area. Through the landscape conservation plan proposed in this study, it is expected that the historical roads and their landscape will be positioned as a modern asset and an aspect of local heritage, and the future conservation and management of the roads and roadscapes will continue.

• Journal of the Korean Society of Mineral and Energy Resources Engineers
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• v.55 no.6
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• pp.553-563
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• 2018

In this study, the location conditions and optimal technologies required for creating urban municipalities that can utilize the space in an abandoned mine area, where there is no infrastructure related to recycling wastes and valuable metals, are investigated. The urban mining industry deals with mineral resources through the processing of high value-added industrial by-products and wastes, and it is a useful linkage industry for the development of mineral resources and prevention of mining hazards. Urban mining technologies targeted at the abandoned mine area constitute screening, extraction, and smelting for recycling waste products. By analyzing the technologies available, an industrial network can be developed for recycling waste batteries and catalysts, which are promising raw materials. It is also important to establish an appropriate location for related industries that can generate value-added resources, rather than the resource supply and demand conditions seen in general urban mines. In order to overcome the accessibility and infrastructure limitations, the economic foundation of the abandoned mine area should consider the linkage of raw material supply, key technologies for recycling useful mineral resources that are derived from urban mines, spatial and site conditions, and industrial characteristics.

• Korean Journal of Environment and Ecology
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• v.35 no.2
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• pp.193-203
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• 2021

As endangered species are gradually increasing due to land development by humans, it is essential to secure sufficient protected areas (PAs) proactively. Therefore, this study checked priority conservation areas to select candidate PAs when considering the impact of land development. We determined the conservation priorities by analyzing four scenarios based on existing conservation areas and reflecting the development impact using MARXAN, the decision-making support software for the conservation plan. The development impact was derived using the developed area ratio, population density, road network system, and traffic volume. The conservation areas of endangered species were derived using the data of the appearance points of birds, mammals, and herptiles from the 3rd National Ecosystem Survey. These two factors were used as input data to map conservation priority areas with the machine learning-based optimization methodology. The result identified many non-PAs areas that were expected to play an important role conserving endangered species. When considering the land development impact, it was found that the areas with priority for conservation were fragmented. Even when both the development impact and existing PAs were considered, the priority was higher in areas from the current PAs because many road developments had already been completed around the current PAs. Therefore, it is necessary to consider areas other than the current PAs to protect endangered species and seek alternative measures to fragmented conservation priority areas.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.59-66
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• 2021

Humans mainly recognize surrounding objects using visual and auditory information among the five senses (sight, hearing, smell, touch, taste). Major research related to the latest object recognition mainly focuses on analysis using image sensor information. In this paper, after emitting various chirp audio signals into the observation space, collecting echoes through a 2-channel receiving sensor, converting them into spectral images, an object recognition experiment in 3D space was conducted using an image learning algorithm based on deep learning. Through this experiment, the experiment was conducted in a situation where there is noise and echo generated in a general indoor environment, not in the ideal condition of an anechoic room, and the object recognition through echo was able to estimate the position of the object with 83% accuracy. In addition, it was possible to obtain visual information through sound through learning of 3D sound by mapping the inference result to the observation space and the 3D sound spatial signal and outputting it as sound. This means that the use of various echo information along with image information is required for object recognition research, and it is thought that this technology can be used for augmented reality through 3D sound.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.561-580
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• 2021

This study analyses the historical and cultural streets at Pinggang Road in the city of Suzhou, by understanding the development and conservation of the area, and uses the following ways to investigate its development, re-organization, and current state. This paper comprehensively compares, collates and investigates 4 different historical and cultural areas in Insadong and Samcheong-dong in South Korea, and South Luogu Lane in China. From initial research and analysis, this paper gathers the cultural, economic, and societal perspectives as non-physical measures, and spatial structure, road structure, and building maintenance as physical factor framework. It is significant in that it can provide an evaluation model for the preservation and regeneration of historical and cultural streets by presenting the viewpoint of complex development of non-physical and physical elements in Pyeonggang-ro. In addition, it is necessary to conduct optimization and specific research on insufficient areas, such as maintenance and development of programs and signature systems for visitors, and continuous development of historical and cultural network platforms by combining on-site surveys. Basic data should be provided for reference on the street.

• Economic and Environmental Geology
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• v.54 no.6
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• pp.615-627
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• 2021

The Baekildo fault, a dextral strike-slip fault developed in Baekil Island, Goheung-gun, controls the distribution of tuffaceous sandstone and lapilli tuff and shows a complex fracture system around it. In this study, we examined the spatial variation in the geometry and connectivity of the fracture system by using circular sampling and topological analysis based on a detailed fracture trace map. As a result, both intensity and connectivity of the fracture system are higher in tuffaceous sandstone than in lapilli tuff. Furthermore, the degree of the orientation dispersion, intensity, and average length of fracture sets vary depending on the along-strike variation in structural position in the tuffaceous sandstone. Notably, curved fractures abutting the fault at a high angle occur at a fault bend. Based on the detailed observation and analyses of the fracture system, we conclude as follows: (1) the high intensity of the fracture system in the tuffaceous sandstone is caused by the higher content of brittle minerals such as quartz and feldspar. (2) the connectivity of the fracture system gets higher with the increase in the diversity and average length of the fracture sets. Finally, (3) the fault bend with geometric irregularity is interpreted to concentrate and disturb the local stress leading to the curved fractures abutting the fault at a high angle. This contribution will provide important insight into various geologic and structural factors that control the development of fracture systems around faults.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1407-1422
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• 2022

This study was conducted to classify tree species and assess the classification accuracy, using SE-Inception, a classification-based deep learning model. The input images of the dataset used Worldview-3 and GeoEye-1 images, and the size of the input images was divided into 10 × 10 m, 30 × 30 m, and 50 × 50 m to compare and evaluate the accuracy of classification of tree species. The label data was divided into five tree species (Pinus densiflora, Pinus koraiensis, Larix kaempferi, Abies holophylla Maxim. and Quercus) by visually interpreting the divided image, and then labeling was performed manually. The dataset constructed a total of 2,429 images, of which about 85% was used as learning data and about 15% as verification data. As a result of classification using the deep learning model, the overall accuracy of up to 78% was achieved when using the Worldview-3 image, the accuracy of up to 84% when using the GeoEye-1 image, and the classification accuracy was high performance. In particular, Quercus showed high accuracy of more than 85% in F₁ regardless of the input image size, but trees with similar spectral characteristics such as Pinus densiflora and Pinus koraiensis had many errors. Therefore, there may be limitations in extracting feature amount only with spectral information of satellite images, and classification accuracy may be improved by using images containing various pattern information such as vegetation index and Gray-Level Co-occurrence Matrix (GLCM).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.367-380
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• 2022

In this study, the periodic and simplified update and production way of the ECVAM (Environmental Conservation Value Assessment Map) was presented through the classification of environmental values using KOMPSAT-3A satellite imagery and land cover map. ECVAM is a map that evaluates the environmental value of the country in five stages based on 62 legal evaluation items and 8 environmental and ecological evaluation items, and is provided on two scales: 1:25000 and 1:5000. However, the 1:5000 scale environmental assessment map is being produced and serviced with a slow renewal cycle of one year due to various constraints such as the absence of reference materials and different production years. Therefore, in this study, one of the deep learning techniques, KOMPSAT-3A satellite image, SI (Spectral Indices), and land cover map were used to conduct this study to confirm the possibility of establishing an environmental assessment map. As a result, the accuracy was calculated to be 87.25% and 85.88%, respectively. Through the results of the study, it was possible to confirm the possibility of constructing an environmental assessment map using satellite imagery, optical index, and land cover classification.

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

• Geophysics and Geophysical Exploration
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• v.25 no.2
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• pp.59-70
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• 2022

Recent studies demonstrate that machine learning has expanded in the field of seismic interpretation. Many convolutional neural networks have been developed for seismic sequence identification, which is important for seismic interpretation. However, expense and time limitations indicate that there is insufficient data available to provide a sufficient dataset to train supervised machine learning programs to identify seismic sequences. In this study, patch division and data augmentation are applied to mitigate this lack of data. Furthermore, to obtain spatial information that could be lost during patch division, an artificial channel is added to the original data to indicate depth. Seismic sequence identification is performed using a U-Net network and the Netherlands F3 block dataset from the dGB Open Seismic Repository, which offers datasets for machine learning, and the predicted results are evaluated. The results show that patch-based U-Net seismic sequence identification is improved by data augmentation and the addition of an artificial channel.