• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.2
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• pp.117-132
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• 2019

The study examines the significance and limits of modern park planning by analyzing major planning documents of Gyeongseong in the Japanese colonial era. Among seven selected documents from 1925 to 1940, which show the contents related with park planning, documents of 1930 and 1940 presented the official park plan of Gyeongseong. By the 1920s, the park plan was not a major concern in urban planning of Gyeongseong; however, as the planning law as enacted in 1934, the park plan legally became a part of the official master planning process in the 1930s. In 1940, the most comprehensive park plan for Gyeongseong was published. In the beginning of modern urban planning, a park was mainly perceived as a sanitation utility. From the 1920s to the 1930s, the park planning system was significantly improved including systemic classification of parks, guideline development considering spatial planning, and introduction of a concept of infra-structural green space. Despite of the improvement in the park planning, the actual quantity of the overall green spaces barely changed and there was a huge discrepancy between the planning ideal and the reality. The Gyeongseong stadium was the only facility newly built in the 1920s, and only two parks were constructed in the 1930s. The plan to build 38 new parks in the 1930, and 140 in the 1940 was barely realized. However, there were efforts to improve parks and green spaces of Gyeongseong: Such as appropriating natural forest as parks, designating royal palaces as parks, and focusing on constructing smaller scale children's parks. Even though the ideal plan could not be fully implemented due to the war time situation and tight budget, the park system of Gyeongseong provided the framework of park planning of Seoul after the independence.

• Korean Journal of Environment and Ecology
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• v.33 no.1
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• pp.75-85
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• 2019

The purpose of this study is to understand the vegetation characteristics of ridges (Gyeongsusan-Seonunsan-Gaeipalsan) in the Seonunsan Provincial Park and to establish reference information for the management of the park in the future. We designated 62 plots with the area of $100m^2$ were installed and analyzed them to investigate the vegetation characteristics. The results of community classification based on TWINSPAN showed seven categories of vegetation communities in the surveyed region: Quercus dentata-Deciduous broad-leaved Community, Quercus variabilis-Pinus thunbergii-Quercus serrata Community, Pinus densiflora Community, Deciduous broad-leaved Community-I, Carpinus tschonoskii-Castanea crenata-Quercus aliena Community, Deciduous broad-leaved Community-II, and Carpinus tschonoskii-Carpinus laxiflora Community. In the vegetation of Seonunsan Provincial Park, coniferous trees such as Pinus thunbergii and Pinus densiflora have been gradually losing their population as part of ecological succession to deciduous broad-leaved trees such as Quercus spp., Carpinus tschonoskii, and Carpinus laxiflora. Moreover, Carpinus turczaninowii, Mallotus japonicus, and others were identified as vegetation reflecting the geographical characteristics of the region neighboring the west coast. The estimated age is 30-60 years, and the oldest tree Pinus densiflora is 63-years old. The index of diversity ($100m^2$) was 0.7942 for Carpinus tschonoskii-Carpinus laxiflora Community, 0.8406 for Carpinus tschonoskii-Castanea crenata-Quercus aliena Community, 0.8543 for Quercus dentata-Deciduous broad-leaved Community, 0.9434 for Quercus variabilis-Pinus thunbergii-Quercus serrata Community, 0.9520 for Deciduous broad-leaved Community-I, 0.9633 for Pinus densiflora Community, and 1.0340 for Deciduous broad-leaved Community-II in the ascending order.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."