• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.2
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• pp.55-67
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• 2023

This study aims to develop a model that can automatically identify the rooftop shape of apartment buildings using GIS and machine learning algorithms, and apply it to analyze the relationship between rooftop shape and characteristics of apartment complexes. A database of rooftop data for each building in an apartment complex was constructed using geospatial data, and individual buildings within each complex were classified into flat type, tower type, and mixed types using the random forest algorithm. In addition, the relationship between the proportion of rooftop shapes, development density, height, and other characteristics of apartment complexes was analyzed to propose the potential application of geospatial information in the real estate field. This study is expected to serve as a basic research on AI-based building type classification and to be utilized in various spatial and real estate analyses.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.109-126
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• 2023

Early detection of forest fires is essential in preventing large-scale forest fires. Predicting forest fires serves as a vital early detection method, leading to various related studies. However, many previous studies focused solely on climate and geographic factors, overlooking human factors, which significantly contribute to forest fires. This study aims to develop forest fire prediction models that take into account human, weather and geographical factors. This study conducted a comparative analysis of four machine learning models alongside the logistic regression model, using forest fire data from Gangwon-do spanning 2003 to 2020. The results indicate that XG Boost models performed the best (AUC=0.925), closely followed by Random Forest (AUC=0.920), both of which are machine learning techniques. Lastly, the study analyzed the relative importance of various factors through permutation feature importance analysis to derive operational insights. While meteorological factors showed a greater impact compared to human factors, various human factors were also found to be significant.

• Journal of the Korean association of regional geographers
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• v.6 no.3
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• pp.101-115
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• 2000

This study aims to make a research on the secondary school education of geography and the system of teacher training in Belgium, focused on the case of Francophone Community. What has been made clear by this research can be summed up as follows. The first two years of the secondary school offer two hours of 'environment education', per week, which can be categorized into the learning of living geography, in that at this stage students learn how to observe the geographic phenomena in their daily life and pigeonhole them. The two years of the second stage of the secondary school offer one hour of 'world geography' which actually is focused on the district of Europe and Russia. The two years of the third stage of the secondary school offer an advanced course of geography which aims to teach systematically the physical geography and the human geography. A remarkable change in geographic education in Belgium is that in the wake of the Revision Act of the secondary school education, textbooks were replaced by other teaching manuals adapted to the regional condition by the teachers. This may result in a wide gap of achievements in geography according to the conditions of educational establishments. Another notable change is that the stress of geographic education tends to be placed on the ability of acquiring practical geographic knowledge rather than the geographic information itself. And it is also another marked tendency that most learning activities in geography class are conducted on the basis of student-centered and the method of investigation. Teachers of the lower secondary schools in Belgium are trained in the School of Education as multi-major teachers, such as a teacher for biology-chemistry-geography or a teacher for history-sociology-geography. Teachers of the higher secondary school education are trained in the Department of Teacher Education in universities as solo-major teachers in that they are required to know more deeply to teach an advanced course of geography in the higher secondary schools. To improve the teacher education many folds of policies are adopted. One is that many in-service teachers are officially put into services of guiding and teaching teacher training. Another is that faculty members in charge of teacher training course are trying to level up the qualifications of teachers by rigorous disciplining.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.3
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• pp.102-113
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• 2016

This study analyzes the geographic information system (GIS) and machine learning models to understand the relationship between the appearance of hibernation sites and habitats in order to systematically manage the habitat of Asiatic Black Bear(Ursus thibetanus ussuricus) inhabiting Jirisan National Park, South Korea. The most important environmental factors influencing the hibernation sites was found to be the inclination(41.4%), followed by altitude(20.4%), distance from the trail(10.9%), and age group(7.7%) in the order of their contribution. A comparison between the hibernation habitat and the normal habitat of Asiatic Black Bear indicated that the average altitude of the hibernation sites was 63m, whereas the average altitude of the normal habitat was approximately 400m. The average inclination was found to be $7^{\circ}$, and a preference for the steeper inclination of $12-43^{\circ}$ was also observed. The average distance of the hibernation site from the road was approximately 300m; the range of separation distance was found to be 1,300-2,400m. This was thought to be the result of a safer selection of winter hibernation site by preventing human contact and outside invasion. This study analyzes the habitat environmental factors for the selection of hibernation sites that prevent severe cold and other threats during the hibernation period in order to provide fundamental data for hibernation ecology and habitat management of Asiatic Black Bear.

• Journal of the Korean Geographical Society
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• v.51 no.3
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• pp.327-344
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• 2016

We may have an incorrect view of Korea and the world by misunderstanding them with a number of geographic misconceptions. Such misconceptions constructed socially tend to perpetuate through reproducing and learning repeatedly from one generation to the next. 'Daegu city is in the intermontane basin.' It is also identified that this geographic misconception had constructed (made) by two Japanese geographers (Tamura, 1933; Tada, 1940) in the Japanese colonial period, and have been reproduced and diffused by many Korean geographers (professors and teachers) as well as journalists in the post-colonial days. In terms of the definition of an intermontane basin in the Encyclopedia of Geomorphology published by the International Association of Geomorphologists, Daegu seems not to be a basin city but to be a plain city, since the central plain of Daegu is surrounded by higher terrain like mountains and hills only on the north and south directions of all sides, and also it is well developed thanks to its location where the downstream of the Geumhogang river flows from east to west. This paper hopes that the landform recognition as 'Daegu intermontane basin city' should be corrected as soon as possible, and also many geographic misconceptions will be studied actively for an accurate understanding of Korea and the world.

• Journal of the Korean Society of Earth Science Education
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• v.15 no.1
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• pp.27-46
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• 2022

The purpose of this study was to examine the perceptions of novelty space among pre-service elementary and secondary earth science teachers. We conducted a survey to explore the perceptions of 38 pre-service elementary school teachers at the National University of Education and 31 pre-service secondary earth science teachers at the Department of Earth Science Education at B University. Semi-structured interviews were conducted with 12 participants, including three pre-service elementary teachers and nine pre-service secondary science teachers. In addition to the elements of novelty space, prior knowledge (cognition), prior outdoor learning experience (psychology), familiarity (geography) with outdoor field learning, and social and technical elements were added. When classified based on elementary and secondary levels, there were statistically significant differences in cognitive, psychological, geographic, and social areas for the elements of novelty space. Statistical differences indicated that the experience or capital related to outdoor learning may have resulted from more pre-service secondary earth science teachers than pre-service elementary teachers. In additional interviews, both elementary and secondary pre-service teachers reported that competencies in the technical domain would be emphasized in the future owing to the necessity and the technical development of virtual-reality-based outdoor field learning programs. This study emphasizes the academic significance of novelty space that should be considered to conduct geological field learning for elementary and secondary earth science pre-service teachers while considering the current post-pandemic educational context.

• Journal of the Korean association of regional geographers
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• v.11 no.2
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• pp.263-277
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• 2005

Since the 1980's, metacognition has been one of the core subjects in the studies on teaching-learning. There have been significant considerations about the metacognition in the reaching-learning become increasingly important in relation with learner's thinking. Though, metacognition has now become important concept used in learning process, there have not been sufficient researches in geographic education. The purpose of this parer is to define metacognition concept in geograpbic education. First, the concept of metacognition in geograpbic education, alike in the other education, can be classified as metacognitive knowledge and metacognitive function. Metacognitive knowledge can be categorized as knowledge about self, task, and strategy. Metacognitive function can be categorized as function about monitoring, evaluating and controling. Next, based on geographical metacognition concept, this paper is researched the characteristics of geographical metacognition in the students' reading maps inquiry activity.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.535-544
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• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.1
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• pp.1-14
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• 2021

The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer's habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.