• Proceedings of the KSRS Conference
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• v.2
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• pp.570-573
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• 2006

College GIS and remote sensing education usually consists of commercial software packages implementations in the classroom. Computer programming is quite important when college graduates work in private or public sectors relevant with GIS and remote sensing implementations. The objective of this paper was to develop a valley management system which implements GIS and remote sensing as the key components for education. The Valley Authority is entitled with water resource protection for sustainable drinking water supply of the second largest city in Taiwan. The test area consists of three different government agencies, Forest Service, EPA, and Water Resource Agency. Materials were provided by the Valley Authority in ArcGIS file format. MapObjects have made the GIS development process much easier. Remote sensing with image manipulation functions were provided by computer programming with Visual Baisc.NET and Visual C#.NET. Attributes inquiry are performed by these two computer languages as well. ArcGIS and ArcPad are also used for simple GIS manipulations of the test area. Comparison between DIY and commercial GIS can be made by college students. Functions provided by the developed valley management system depending on how many map layers have been used and what types of MapObjects components have been used. Computer programming experience is not essential but can be helpful for a college student. The whole process is a step-by-step sequence which college students can modify to depict their capability in GIS and remote sensing. The development process has gone through one semester, three hours every week in 18 weeks. College students enrolled in this class entitled with GIS showed remarkable progresses both in GIS and remote sensing.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.131-139
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• 2010

Recent spread of GIS and the increasing demand of spatial data have brought about the development of web GIS. In addition to sharing and mapping spatial data, web GIS is also required to provide spatial analytic functions on the web. The FOSS(free and open source software) can play an important role in developing such a system for web GIS. In this paper, we proposed a web-based system for raster data analysis using map algebra. We employed GRASS as an open source software and implemented the GRASS functionalities on the web using java methods for invocation of server-side commands. Map algebra and AHP were combined for the raster data analysis in our system. For a feasibility test, the landslide susceptibility in South Korea was calculated using rainfall, elevation, slope angle, slope aspect, and soil layers. It is anticipated that our system will be extensible to other web GIS for raster data analysis with GRASS.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.447-454
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• 2018

This paper proposes a depth image generation algorithm of stereo images using a deep learning model composed of a CNN (convolutional neural network). The proposed algorithm consists of a feature extraction unit which extracts the main features of each parallax image and a depth learning unit which learns the parallax information using extracted features. First, the feature extraction unit extracts a feature map for each parallax image through the Xception module and the ASPP(Atrous spatial pyramid pooling) module, which are composed of 2D CNN layers. Then, the feature map for each parallax is accumulated in 3D form according to the time difference and the depth image is estimated after passing through the depth learning unit for learning the depth estimation weight through 3D CNN. The proposed algorithm estimates the depth of object region more accurately than other algorithms.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.29-35
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• 2010

Soil organic carbon (SOC), being a help to forest formation and control of carbon dioxide in the air, is found to be an important factor by which global warming is influenced. Excavating the samples by whole area is very inefficient method to discovering the distribution of SOC. So, the development of suitable model for expecting the relative amount of the SOC makes better use of expecting the SOC. In the present study, a model based on a decision tree algorithm is introduced to estimate the amount of SOC along with accessing influencing factors such as altitude, aspect, slope and type of trees. The model was applied to a real site and validated by 10-fold cross validation using two softwares, See 5 and Weka. From the results given by See 5, it can be concluded that the amount of SOC in surface layers is highly related to the type of trees, while it is, in middle depth layers, dominated by both type of trees and altitude. The estimation accuracy was rated as 70.8% in surface layers and 64.7% in middle depth layers. A similar result was, in surface layers, given by Weka, but aspect was, in middle depth layers, found to be a meaningful factor along with types of trees and altitude. The estimation accuracy was rated as 68.87% and 60.65% in surface and middle depth layers. The introduced model is, from the tests, conceived to be useful to estimation of SOC amount and its application to SOC map production for wide areas.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.515-523
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• 2021

An integrated underground spatial map consists of underground facilities, underground structures, and ground information, and is periodically updated. In this paper, we design and implement a system for detecting and extracting only changed ground objects to shorten the map update speed. To find the changed objects, all the objects are compared, which are included in the newly input map and the reference map in the integrated map. Since the entire process of comparing objects and generating results is classified by function, the implemented system is composed of several modules such as object comparer, changed object detector, history data manager, changed object extractor, changed type classifier, and changed object saver. We use two metrics: detection rate and extraction rate, to evaluate the performance of the system. As a result of applying the system to boreholes, ground wells, soil layers, and rock floors in Pyeongtaek, 100% of inserted, deleted, and updated objects in each layer are detected. In addition, it provides the advantage of ensuring the up-to-dateness of the reference map by downloading it whenever maps are compared. In the future, additional research is needed to confirm the stability and effectiveness of the developed system using various data to apply it to the field.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.21-29
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• 2020

Accurate estimation of human pose relies on backbone method in which its role is to extract feature map. Up to dated, the method of backbone feature extraction is conducted by the plain convolutional neural networks named by CNN and the residual neural networks named by Resnet, both of which have various architectures and performances. The CNN family network such as VGG which is well-known as a multiple stacked hidden layers architecture of deep learning methods, is base and simple while Resnet which is a bottleneck layers architecture yields fewer parameters and outperform. They have achieved inspired results as a backbone network in human pose estimation. However, they were used then followed by different pose estimation networks named by pose parsing module. Therefore, in this paper, we present a comparison between the plain CNN family network (VGG) and bottleneck network (Resnet) as a backbone method in the same pose parsing module. We investigate their performances such as number of parameters, loss score, precision and recall. We experiment them in the bottom-up method of human pose estimation system by adapted the pose parsing module of openpose. Our experimental results show that the backbone method using VGG network outperforms the Resent network with fewer parameter, lower loss score and higher accuracy of precision and recall.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.775-779
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• 2002

Currently, high-resolution satellite imagery such as KOMPSAT and IKONOS has been tentatively utilized to various types of urban engineering problems such as transportation planning, site planning, and utility management. This approach aims at software development and followed applications of remotely sensed imagery to transportation geographic analysis. At first, GDPA (Gradient Direction Profile Algorithm) and main modules in it are overviewed, and newly implemented results under MS visual programming environment are presented with main user interface, input imagery processing, and internal processing steps. Using this software, road network are automatically generated. Furthermore, this road network is used to transportation geographic analysis such as gamma index and road pattern estimation. While, this result, being produced to do-facto format of ESRI-shapefile, is used to several types of road layers to urban/transportation planning problems. In this study, road network using KOMPSAT EOC imagery and IKONOS imagery are directly compared to multiple road layers with NGI digital map with geo-coordinates, as ground truth; furthermore, accuracy evaluation is also carried out through method of computation of commission and omission error at some target area. Conclusively, the results processed in this study is thought to be one of useful cases for further researches and local government application regarding transportation geographic analysis using remotely sensed data sets.

• The Journal of Engineering Geology
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• v.33 no.3
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• pp.475-487
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• 2023

We surveyed the distribution of soil layers on Dongdo and Seodo of Dokdo and measured the physical properties of the soils. To investigate the distribution of soil layers, the soil depth was measured directly in accessible locations, and visual observations of inaccessible locations were carried out using drones and boats. Soil depths ranged from 3 to 50 cm, and most soil layers had depths of 10~20 cm. Based on these results, a map of the soil layer was drawn using 5 cm intervals for soil depth. To analyze the soil characteristics of Dokdo, soil samples were collected from 13 locations on Dongdo and 13 locations on Seodo, in consideration of various geological settings. According to the results of grain size distribution tests, sand contents were >75%, and soil from Seodo contained more gravel-sized particles than that from Dongdo. Using the unified soil classification system (USCS) and textural classification chart of the United States Department of Agriculture (USDA), most of the soil samples from Dokdo are classified as sand, and some are classified as loamy or clayey sand. In addition, well-graded loamy or clayey sands are more common in Dongdo, and poorly graded sands with gravel are more common in Seodo. These results are expected to be important for studying soil characteristics on Dokdo.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.324-326
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• 2008

When we urgently need to develop and supply an alternative energy, wind power is growing with much interest because it has relative low cost of power and area of tower. To estimate the wind power resource, it is necessary to make an wind resource map first. On the study of wind resource map in the Korean peninsula, Southern coast was needed to investigate the possibility of developing wind power complex because of good wind resources. In this study, we made a vertical observation to analyze the properties of wind in coastal area. From tethered sonde observation, we knew that synoptic effect had an influence higher in second day than first day. This means local wind circulation is generated on first day but not second day. The local wind made vertical wind shear strong in first day. Also, there was large difference of wind speed between layers at night time by analysis of SODAR observation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1831-1836
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• 2014

Probabilistic liquefaction hazard map is now widely needed for engineering practice. Based on the Liquefaction Potential Index (LPI) calculated from liquefied and non-liquefied cases, we attempted to estimate probabilities of liquefaction induced ground failures using logistic regression. We then applied this approach for the regional area. LPIs were calculated based on 273 Standard Penetration Tests in the floodplains in the St. Louis area, USA and then interpolated using cokriging with the covariable of peak ground acceleration. Our result shows that some areas of $LPI{\geq}5$, due to soft soil layers and shallow groundwater table, appear probabilities of ground $failure{\geq}0.5$.