• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.96-109
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• 2011

Recently, the geographic information system(GIS) is generally used in various fields with the development of information and communication technology, with expansion of its applications and utilization scope. Especially, utilizing GIS is expected to have positive effects on the geography learning and more helpful for the geographic information observation compared to the picture or 2D based media. The effective visualization of complex geographic data does not only take realization of its visual information but also increases the human ability in analysis and understanding to use the geographic information. In this paper, we examine a method to develop the geography learning contents based on the technology with augmented reality and GIS, and then we have a case study for various kinds of visualization techniques and examples to use in geography learning situation. Moreover, we introduce an example of the manufacturing process from the existing GIS data to augmented reality based geography learning system. From the above, we show that the usefulness of our method is applicable for effective visualization of the three-dimensional geographic information in the geography learning environment.

• Journal of the Korean Geographical Society
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• v.39 no.6 s.105
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• pp.971-983
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• 2004

It is increasingly clear that student mastery of concepts and skills in geographic education is based on a complex set of variables. Sex and gender are the key variables. Much has been written about biological sex differences in learning, but less attention has been paid to the impacts of socio-cultural gender on learning geography. As such, the aims of this paper are two-fold. First, to examine theories which seek to explain why males and females might differ in their geographic and spatial knowledge or skill. Second, to examine the extent of sex differences and gender traits in the geographic learning. The results of study illustrate clearly that there are more similarities than differences between the sexes. Therefore, there are significant gender differences between the preferences of regions, contents, activities in the secondary geographic learning. The results also provide insights into improving contents and method of geographic education.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.437-456
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• 2021

In the field of remote sensing in Korea, starting in 2017, deep learning has begun to show efficient research results compared to existing research methods. Currently, research is being conducted to apply deep learning in almost all fields of remote sensing, from image preprocessing to applications. To analyze the research trend of deep learning applied to the remote sensing field, Korean domestic journal papers, published until October 2021, related to deep learning applied to the remote sensing field were collected. Based on the collected 60 papers, research trend analysis was performed while focusing on deep learning network purpose, remote sensing application field, and remote sensing image acquisition platform. In addition, open source data that can be effectively used to build training data for performing deep learning were summarized in the paper. Through this study, we presented the problems that need to be solved in order for deep learning to be established in the remote sensing field. Moreover, we intended to provide help in finding research directions for researchers to apply deep learning technology into the remote sensing field in the future.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.61-72
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• 2020

Recently, researches that are using deep learning technology in various fields are being conducted. The fields include geographic map processing. In this paper, I propose a method to infer where the map area included in the image is. The proposed method generates and learns images including a map, detects map areas from input images, extracts character strings belonging to those map areas, and converts the extracted character strings into coordinates through geocoding to infer the coordinates of the input image. Faster R-CNN was used for learning and map detection. In the experiment, the difference between the center coordinate of the map on the test image and the center coordinate of the detected map is calculated. The median value of the results of the experiment is 0.00158 for longitude and 0.00090 for latitude. In terms of distance, the difference is 141m in the east-west direction and 100m in the north-south direction.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.1-9
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• 2021

In this study, we evaluated the accuracy of deep learning-based tree species classification model trained by using high-resolution images. We selected five species classed, i.e., pine, birch, larch, korean pine, mongolian oak for classification. We created 5,000 datasets using high-resolution orthophoto and forest type map. CNN deep learning model is used to tree species classification. We divided training data, verification data, and test data by a 5:3:2 ratio of the datasets and used it for the learning and evaluation of the model. The overall accuracy of the model was 89%. The accuracy of each species were pine 95%, birch 89%, larch 80%, korean pine 86% and mongolian oak 98%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.409-418
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• 2021

As the amount of construction for aerial photography increases, the need for automation of quality inspection is emerging. In this study, an experiment was performed to classify or detect clouds in aerial photos using deep learning techniques. Also, classification and detection were performed by including satellite images in the learning data. As algorithms used in the experiment, GoogLeNet, VGG16, Faster R-CNN and YOLOv3 were applied and the results were compared. In addition, considering the practical limitations of securing erroneous images including clouds in aerial images, we also analyzed whether additional learning of satellite images affects classification and detection accuracy in comparison a training dataset that only contains aerial images. As results, the GoogLeNet and YOLOv3 algorithms showed relatively superior accuracy in cloud classification and detection of aerial images, respectively. GoogLeNet showed producer's accuracy of 83.8% for cloud and YOLOv3 showed producer's accuracy of 84.0% for cloud. And, the addition of satellite image learning data showed that it can be applied as an alternative when there is a lack of aerial image data.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.183-201
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• 2022

Estimating the implicit value of housing assets is a very important task for participants in the housing market. Until now, such estimations were usually carried out using multiple regression analysis based on the inherent characteristics of the estate. However, in this paper, we examine the estimation capabilities of the Artificial Neural Network(ANN) and its 'Deep Learning' faculty. To make use of the strength of the neural network model, which allows the recognition of patterns in data by modeling non-linear and complex relationships between variables, this study utilizes geographic coordinates (i.e. longitudinal/latitudinal points) as the locational factor of housing prices. Specifically, we built a dataset including structural and spatiotemporal factors based on the hedonic price model and compared the estimation performance of the models with and without geographic coordinate variables. The results show that high estimation performance can be achieved in ANN by explaining the spatial effect on housing prices through the geographic location.

• Journal of the Korean Geographical Society
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• v.33 no.1
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• pp.109-122
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• 1998

The purpose of this study is to develop value instruction program which can deal with geographic value problem(GVP) in geographic education. This program is organized into seven stages: identification of GVP(reading of content of GVP, and categorization and description of action which involved in content), analysis of GVP(comparison and analysis of GVP, and ordering of value positions), decision making, justification of decision making and actualization. The processes of decision making and their related activities are emphasized in this program. In experimental classroom, it took effects to providing subjective experiences with students, developing decision making ability, and giving responsibility of decision making. Therefore this study suggests that this program helps students to improve their social participation ability as the democratic citizenship.

• International Journal of Contents
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• v.12 no.4
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• pp.8-16
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• 2016

This research proposes the composition logic of an Active Learning Environment (ALE), to enable discovery by learning through experience, whilst increasing knowledge about modern architectural heritage. Linking information to the historical heritage using Information and Communication Technology (ICT) helps to overcome the limits of previous learning methods, by providing rich learning resources on site. Existing field trips of cultural heritages are created to impart limited experience content from web resources, or receive content at a specific place through humanities Geographic Information System (GIS). Therefore, on the basis of the blended space theory, an augmented space experience method for overcoming these shortages was composed. An ALE space framework is proposed to enable discovery through learning in an expanded space. The operation of ALE space is needed to create full coordination, such as a Content Management System (CMS). It involves a relation network to provide knowledge to the rule engine of the CMS. The application is represented with the Deoksugung Palace Seokjojeon hall example, by describing a user experience scenario.

• Journal of the Korean earth science society
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• v.43 no.3
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• pp.430-445
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• 2022

The purpose of this study was to explore the learning effects in elementary school students who participated in a geological field trip conducted under the theme 'minerals and rocks', focusing on novelty space. A total of 10 sixth-grade students participated in this program held at a public elementary school in Seoul as part of after-school club activities. Students observed mineral and rock samples in a classroom and outdoor learning environment. The authors collected activity papers (texts, drawing), researchers' participation notes, video and audio recordings containing the study participants' activities, and post-interview data To analyze the learning effects in the cognitive domain of students, the observation analysis framework for rock classification of Remmen and Frøyland (2020) and the rock description analysis framework of Oh (2020) were used. Additionally, to explore the learning effects of psychological and geographic areas, students' drawings, texts, discourses, and interview data were inductively analyzed. The results showed that the students demonstrated 'everyday' and 'transitional' observations in the classroom learning environment, while in the outdoor learning environment (school playground, community-based activities), they demonstrated 'transitional' and 'scientific' observations. Moreover, as the scientific observation stage progressed, more types of descriptive words for rocks were used. In terms of psychological and geographic aspects, students showed their selection of places to explore familiar outdoor learning environments, positive perceptions of outdoor learning, and aesthetic appreciation. Finally, this study not only discussed novelty space as a tool for analyzing students' learning effects but also suggested the need for an academic approach considering new learning environments, such as learning through virtual field trips.