• Korean Journal of Heritage: History & Science
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• v.37
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• pp.371-405
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• 2004

International circulation of cultural assets involves numerous countries thereby making an approach based on international law essential to resolving this problem. Since the end of the $2^{nd}$ World War, as the value of cultural assets evolved from material value to moral and ethical values, with emphasis on establishing national identities, newly independent nations and former colonial states took issue with ownership of cultural assets which led to the need for international cooperation and statutory provisions for the return of cultural assets. UNESCO's 1954 "Convention for the Protection of Cultural Property in the Event of Armed Conflict" as preparatory measures for the protection of cultural assets, the 1970 "Convention on the Means of Prohibiting and Preventing the Illicit Import and Transfer of Ownership of Cultural Property" to regulate transfer of cultural assets, and the 1995 "Unidroit Convention on Stolen or Illegally Exported Cultural Objects" which required the return of illegally acquired cultural property are examples of international agreements established on illegal transfers of cultural assets. In addition, the UN agency UNESCO established the Division of Cultural Heritage to oversee cultural assets related matters, and the UN since its 1973 resolution 3187, has continued to demonstrate interest in protection of cultural assets. The resolution 3187 affirms the return of cultural assets to the country of origin, advises on preventing illegal transfers of works of art and cultural assets, advises cataloguing cultural assets within the respective countries and, conclusively, recommends becoming a member of UNESCO, composing a forum for international cooperation. Differences in defining cultural assets pose a limitation on international agreements. While the 1954 Convention states that cultural assets are not limited to movable property and includes immovable property, the 1970 Convention's objective of 'Prohibiting and preventing the illicit import, export and transfer of ownership of cultural property' effectively limits the subject to tangible movable cultural property. The 1995 Convention also has tangible movable cultural property as its subject. On this point, the two conventions demonstrate distinction from the 1954 Convention and the 1972 Convention that focuses on immovable cultural property and natural property. The disparity in defining cultural property is due to the object and purpose of the convention and does not reflect an inherent divergence. In the case of Korea, beginning with the 1866 French invasion, 36 years of Japanese colonial rule, military rule and period of economic development caused outflow of numerous cultural assets to foreign countries. Of course, it is neither possible nor necessary to have all of these cultural properties returned, but among those that have significant value in establishing cultural and historical identity or those that have been taken symbolically as a demonstration of occupational rule can cause issues in their return. In these cases, the 1954 Convention and the ratification of the first legislation must be actively considered. In the return of cultural property, if the illicit acquisition is the core issue, it is a simple matter of following the international accords, while if it rises to the level of diplomatic discussions, it will become a political issue. In that case, the country requesting the return must convince the counterpart country. Realizing a response to the earnest need for preventing illicit trading of cultural assets will require extensive national and civic societal efforts in the East Asian area to overcome its current deficiencies. The most effective way to prevent illicit trading of cultural property is rapid circulation of information between Interpol member countries, which will require development of an internet based communication system as well as more effective deployment of legislation to prevent trading of illicitly acquired cultural property, subscription to international conventions and cataloguing collections.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• (The) Research of the performance art and culture
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• no.25
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• pp.253-289
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• 2012

Dasiraegi is a traditional funeral rite performance of Jindo located in the South Jeolla Province of South Korea. With its unique stylistic structure including various dances, songs and witty dialogues, and a storyline depicting the birth of a new life in the wake of death, embodying the Buddhism belief that life and death is interconnected; it attracted great interest from performance organizers and performers who were desperately seeking new contents that can be put on stage as a performance. It is needless to say previous research on Dasiraegi had been most valuable in its recreation as it analyzed the performance from a wide range of perspectives. Despite its contributions, the previous researches were mainly academic focusing on: the symbolic meanings of the performance, basic introduction to the components of the performance such as script, lyrics, witty dialogue, appearance (costume and make-up), stage properties, rhythm, dance and etc., lacking accurate representation of the most crucial element of the performance which is sori (song). For this reason, the study analyzes the music of Dasiraegi and presents its musical characteristics along with its scores to provide practical support for performers who are active in the field. Out of all the numbers in Dasiraegi, this study analyzed all of Geosa-nori and Sadang-nori, the funeral dirge (mourning chant) sung as the performers come on stage and Gasangjae-nori, because among the five proceedings of the funeral rite they were the most commonly performed. There are a plethora of performance recordings to choose from, however, this study chose Jindo Dasiraegi, an album released by E&E Media. The album offers high quality recordings of performances, but more importantly, it is easy to obtain and utilize for performers who want to learn the Dasiraegi based on the script provided in this study. The musical analysis discovered a number of interesting findings. Firstly, most of the songs in Dasiraegi use a typical Yukjabaegi-tori which applies the Mi scale frequently containing cut-off (breaking) sounds. Although, Southern Kyoung-tori which applies the Sol scale was used, it was only in limited parts and was musically incomplete. Secondly, there was no musical affinity between Ssitgim-gut and Dasiraegi albeit both are for funeral rites. The fundamental difference in character and function of Ssitgim-gut and Dasiraegi may be the reason behind this lack of affinity, as Ssitgim-gut is sung to guide the deceased to heaven by comforting him/her, whereas, Dasiaregi is sung to reinvigorate the lives of the living. Lastly, traces of musical grammar found in Pansori are present in the earlier part of Dasiraegi. This may be attributed to the master artist (Designee of Important Intangible Cultural Heritage), who was instrumental in the restoration and hand-down of Dasiaregi, and his experience in a Changgeuk company. The performer's experience with Changgeuk may have induced the alterations in Dasiraegi, causing it to deviate from its original form. On the other hand, it expanded the performative bais by enhancing the performance aspect of Dasiraegi allowing it to be utilized as contents for Performing Arts. It would be meaningful to see this study utilized to benefit future performance artists, taking Dasiraegi as their inspiration, which overcomes the loss of death and invigorates the vibrancy of life.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.155-170
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• 2021

Oats (Avena sativa L.) represent a good forage crop for cultivation in regions with short growing periods and/or cool weather, such as the mountainous areas of southern Korea. In this study, using the Korean elite summer oat varieties 'High speed' and 'Dark horse', we aimed to determine the optimal time to plant and harvest forage oats seeded in spring and summer in a mountainous area. Seeds were planted three times from late February and early August at 9- or 10-days intervals, respectively, and plants were harvested three times from late May to October at 10-day intervals. The experiment was carried out in an upland field (Jangsu-gun Jeonbuk) in 2015 and 2016. We investigated the changes in forage yield (FY) and quality [crude protein (CP) and total digestible nutrient (TDN) contents] based on the time of planting and harvest. Neither the forage quality nor yield of either spring and summer oats was significantly influenced by the time of planting. The CP of spring oats harvested three times at 10-day intervals from late May was 12.0%, 8.2%, and 6.5%, thereby indicating a reduction with a delay in the time of harvest. In summer oats, CP ranged from 8.4% to 8.7%, although unlike CP in spring oats, was not significantly influenced by the time of harvest. For both forage types, harvest time had no significant effect on TDN. The FY of spring oats harvested in late May and early and mid-June was 10.2, 18.7, and 19.5 ton ha^-1, respectively, with that of oats harvested on the latter two dates being significantly increased by 83% and 91%, respectively, compared with that in late May. Similarly, the FY of spring oats harvested in late October and early and mid-November was 7.1, 12.5, and 12.1 ton ha^-1, respectively, with that of oats harvested on the latter two dates being significantly increased by 75% and 71%, respectively, compared with that in late October. Taking into consideration forage yield and quality (not less than 8% CP), it would be profitable to plant spring oats in the mountainous areas of southern Korea until March 15 and harvest around June 10, whereas summer oats could be beneficially planted until August 25 and harvested from early November.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.329-339
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• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.40 no.1
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• pp.26-42
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• 2022

The purpose of this study is to examine and understand the area and the original outer boundaries of Hamchunwon(含春苑), which was the outer royal garden of Gyeonghuigung Palace, which existed before the site of the Russian legation. The results of the study are as follows. First, examining the 3 types of drawings prepared for securing the Russian legation's site and constructing a new building, it was confirmed that two low peaks, which appear to be the original terrain of Hamchunwon, existed in the north and south directions inside the site. According to the initial plan of the of the legation's site, it appears that the entrance of the legation building is connected to the Saemunan-ro in the northwest. However, according to the report made at the time when the Russian temporary minister Veber purchased the legation's site, it was recorded that the site already had a narrow entrance and a dirt road in place, and hence, it was connected to Saemunan-ro. This fact makes it possible to learn that the line of movement for officials and the original gate were located to the northwest of the site planned as the entrance of the legation building towards Hamchunwon. Second, the site was created by cutting the top of the high hill at the time of the construction of the legation building, and as a result, a two tiered staircase typed terrace was built. The ground on which the main building and the secretary's building, etc., were erected was made by cutting the highest peak and solidifying it flat, and a large quantity of soil was used for grading. In the case of the northern area of the main building, the traces of leveling the terrain by cutting the mountains are apparent, and an observation typed garden with a walking path and pavilion was formed by utilizing the physical environment equipped with an easy view. This may be considered as a use which is consistent with the topographical conditions of creating an outer royal garden to block the civilian views on a high terrain overlooking the palace. Third, Hamchunwon's fences were partially exposed in the photos from the 1880s through the 1890s, which demonstrate the spatial changes made around the US, UK, and the Russian legations. As a result of the photo analysis performed, Hamchunwon occupies the northern area of the Russian legation's site, and it is estimated that the north, west, and east walls of the legation resembled those of Hamchunwon. The area to the south of the Russian legation was originally a place made available for civilian houses, and it was possible to examine the circumstances of purchasing dozens of civilian houses and farmlands according to various materials. Fourth, Hamchunwon, which was formed as the outer royal garden of Gyeongdeokgung Palace of Lord Gwanghaegun, lost its sense of place as an outer royal garden when the entire building of Gyeonghuigung Palace was torn down and used as a construction members during the reconstruction of Gyeongbokgung Palace, and faded away as the site was sold to Russia around 1885. The area where Hamchunwon used to be located transformed into a core space of the Russian legation where the main building and garden were located after the construction of the new building. Hence, Hamchunwon, which was limited to the northern area of the Russian legation, does not carry the temporal and spatial context with Gyeongungung Palace and Seonwonjeon which were constructed after 1897, and it is determined that the view of Seonwonjeon as Baehoorim or Baegyeongrim is not valid.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.39 no.2
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• pp.15-28
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• 2021

'Cheonunjeongsa (天雲精舍)', designated as Gyeongsangbukdo Folklore Cultural Property No. 76, is a Siksanjeongsa built in 1700 by Manbu Lee Shiksan. In this study, we investigate the life and perspective of Manbu Lee in relation to Siksanjeongsa, and estimate the feng shui location, territoriality, and original landscape by analyzing 「Nuhangnok」 and 「Nuhando」, the results of his political management. The following results were derived by examining the philosophy that the scholar wanted to include in his space. First, Manbu Lee Shiksan was a representative hermit-type confucian scholar in the late Joseon Dynasty. 'Siksan', the name of the government official and the nickname of Manbu Lee, is derived from the mountain behind the village, and he wanted to rest in the four areas of thought(思), body(躬), speech(言), and friendship(交). During the difficult years of King Sukjong, Lee Manbu of a Namin family expressed his will to seclude through the title 'Siksan'. Second, There is a high possibility of restoration close to the original. Manbu Lee recorded the location of Siksanjeongsa, spatial structure, buildings and landscape facilities, trees, surrounding landscape, and usage behaviors in 「Nuhangnok」, and left a book of 《Nuhangdo》. Third, Manbu Lee refers to the feng shui geography view that Oenogok is closed in two when viewed from the outside, but is cozy and deep and can be seen from a far when entering inside. The whole village of Nogok was called Siksanjeongsa, which means through the name. It can be seen that the area was formed and expanded. Fourth, the spatial composition of Siksanjeongsa can be divided into a banquet space, an education space, a support space, a rest space, a vegetable and an herbal garden. The banquet space composed of Dang, Lu, and Yeonji is a personal space where Manbu Lee, who thinks about the unity of the heavenly people, the virtue of the gentleman, and humanity, is a place for lectures and a place to live. Fifth, Yangjeongjae area is an educational space, and Yangjeongjae is a name taken from the main character Monggwa, and it is a name that prayed for young students to grow brightly and academically. Sixth, the support space composed of Ganjijeong, Gobandae, and Sehandan is a place where the forested areas in the innermost part of Siksanjeongsa are cleared and a small pavilion is built using natural standing stones and pine trees as a folding screen. The virtue and grace of stopping. It contains the meaning of leisure and the wisdom of a gentleman. Seventh, outside the wall of Siksanjeongsa, across the eastern stream, an altar was built in a place with many old trees, called Yeonggwisa, and a place of rest was made by piling up an oddly shaped stone and planting flowers. Eighth, Manbu Lee, who knew the effects of vegetables and medicinal herbs in detail like the scholars of the Joseon Dynasty, cultivated a vegetable garden and an herbal garden in Jeongsa. Ninth, it can be seen that Lee Manbu realized the Neo-Confucian utopia in his political life by giving meaning to each space of Siksanjeongsa by naming buildings and landscaping facilities and planting them according to ancient events.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.377-388
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• 2022

Laboratory-scale experiments were performed to identify the As removal mechanism of the residual slag generated after the mineral carbonation process. The residual slags were manufactured from the steelmaking slag (blast oxygen furnace slag: BOF) through direct and indirect carbonation process. RDBOF (residual BOF after the direct carbonation) and RIBOF (residual BOF after the indirect carbonation) showed different physicochemical-structural characteristics compared with raw BOF such as chemical-mineralogical properties, the pH level of leachate and forming micropores on the surface of the slag. In batch experiment, 0.1 g of residual slag was added to 10 mL of As-solution (initial concentration: 203.6 mg/L) titrated at various pH levels. The RDBOF showed 99.3% of As removal efficiency at initial pH 1, while it sharply decreased with the increase of initial pH. As the initial pH of solution decreased, the dissolution of carbonate minerals covering the surface was accelerated, increasing the exposed area of Fe-oxide and promoting the adsorption of As-oxyanions on the RDBOF surface. Whereas, the As removal efficiency of RIBOF increased with the increase of initial pH levels, and it reached up to 70% at initial pH 10. Considering the PZC (point of zero charge) of the RIBOF (pH 4.5), it was hardly expected that the electrical adsorption of As-oxyanion on surface of the RIBOF at initial pH of 4-10. Nevertheless it was observed that As-oxyanion was linked to the Fe-oxide on the RIBOF surface by the cation bridge effect of divalent cations such as Ca²⁺, Mn²⁺, and Fe²⁺. The surface of RIBOF became stronger negatively charged, the cation bridge effect was more strictly enforced, and more As can be fixed on the RIBOF surface. However, the Ca-products start to precipitate on the surface at pH 10-11 or higher and they even prevent the surface adsorption of As-oxyanion by Fe-oxide. The TCLP test was performed to evaluate the stability of As fixed on the surface of the residual slag after the batch experiment. Results supported that RDBOF and RIBOF firmly fixed As over the wide pH levels, by considering their As desorption rate of less than 2%. From the results of this study, it was proved that both residual slags can be used as an eco-friendly and low-cost As remover with high As removal efficiency and high stability and they also overcome the pH increase in solution, which is the disadvantage of existing steelmaking slag as an As remover.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.