• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.2
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• pp.159-164
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• 2008

This study simulated the flood inundations of the Nakdong River catchment running through Yangsan, a small city located in the south eastern area of Korea by using the depth averaged two-dimensional hydrodynamic numerical model. The numerical model employs the staggered grid system including moving boundary and a finite different method to solve the Saint-Venant equations. A second order upwind scheme is used to discretize the nonlinear convection terms of the momentum equations, whereas linear terms are discretized by a second order Leap-frog scheme(Cho and Yoon, 1998). The numerical model was applied to a real topography to simulate the flood inundation of the Yangsan basin in Yangsan. The numerical result for urban district was visualization for three dimension. These results can be essentially utilized to construct the three dimensional inundation map after building the GIS-based database in local public organizations in order to protect the life and property safely.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.558-573
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• 2024

Considering factors such as illumination, camera quality variations, and background-specific variations, identifying a face using a smartphone-based facial image capture application is challenging. Face Image Quality Assessment refers to the process of taking a face image as input and producing some form of "quality" estimate as an output. Typically, quality assessment techniques use deep learning methods to categorize images. The models used in deep learning are shown as black boxes. This raises the question of the trustworthiness of the models. Several explainability techniques have gained importance in building this trust. Explainability techniques provide visual evidence of the active regions within an image on which the deep learning model makes a prediction. Here, we developed a technique for reliable prediction of facial images before medical analysis and security operations. A combination of gradient-weighted class activation mapping and local interpretable model-agnostic explanations were used to explain the model. This approach has been implemented in the preselection of facial images for skin feature extraction, which is important in critical medical science applications. We demonstrate that the use of combined explanations provides better visual explanations for the model, where both the saliency map and perturbation-based explainability techniques verify predictions.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.15-27
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• 2020

Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster-Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future social services and amenities for the local inhabitants.

• Journal of Cadastre & Land InformatiX
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• v.54 no.2
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• pp.279-288
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• 2024

The traditional methods of constructing 3D spatial information have involved obtaining spatial data through MMS or remote sensing based on aerial and satellite platforms, followed by post-processing. However, when applying the existing semi-automatic post-processing methods to urban areas with numerous geographical features such as buildings, the costs can become excessively high. As a result, there is a growing demand for more efficient technologies to construct 3D spatial information. This study explores a cost-saving method by mapping newly constructed 3D spatial information-based on drone data or the Building Height Database-onto pre-existing 3D models established by local governments. Additionally, the study investigates potential applications in the field of UAM.

• Spatial Information Research
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• v.19 no.5
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• pp.47-52
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• 2011

Recently damage scale by local winds and typhoon has dramatically increased. Korea has the terrain over 70% of the land and the planning of the wind load is necessary to estimate reflecting appropriately the change of the wind-speed according to the characteristic of the terrain and in the Korean Building Code(2009), this is stated and it reflects to the design process. However, in order to estimate the topographic factor of the wind speed considering the topographic characteristics in the structure design actually, it has many difficult points including the local topographic survey, etc. In this paper, the Digital Elevation Model(DEM) is created using TIN interpolation method in the form of the digital map and then the interface was designed and implemented which can automatically estimate the topographic factor of wind speed by using ESRI(R)ArcObjectTM and the Visual Basic programing language. By applying it to the terrain which positioned in the downtown area, the practicality of the topographic factor of wind speed estimation interface was checked.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.669-676
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• 2018

As the Moon's scientific, technological, and economic value has increased, major space agencies around the world are leading lunar exploration projects by establishing a road map to develop lunar resources and to construct a lunar base. In addition, as the lunar base construction requires huge amounts of resources from the Earth, lunar in-situ construction technology is being developed to produce construction materials from local lunar resources. On the other hand, the characteristics of lunar topography and resources vary spatially due to the crustal and volcanic activities inside the Moon as well as the solar wind and meteorites from outside the Moon. Therefore, in this paper, the geospatial analysis of lunar resource distribution was conducted to suggest regional consideration factors to apply the lunar in situ construction technologies. In addition, the lunar topographic condition to select construction sites was suggested to ensure the safe landing of a lunar lander and the easy maneuvering of a rover. The lunar topographic and resource information mainly from lunar orbiters were limited to the lunar surface with a low spatial resolution. Rover-based lunar exploration in the near future is expected to provide valuable information to develop lunar in situ construction technology and select candidate sites for lunar base construction.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.40-50
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• 2010

The building process of any geological map involves linking sparse lithological outcrop information with equally sparse geometrical measurements, all in a single entity which is the preferred interpretation of the field geologist. The actual veracity of this interpretative map is partially dependent upon the frequency and distribution of geological outcrops compounded by the complexity of the local geology. Geophysics is commonly used as a tool to augment the distribution of data points, however it normally does not have sufficient geometrical constraints due to: a) all geophysical inversion models being inherently non-unique; and b) the lack of knowledge of the physical property contrasts associated with specific lithologies. This contribution proposes the combined use of geophysical edge detection routines and 'three point' solutions from topographic data as a possible approach to obtaining geological contact geometry information (strike and dip), which can be used in the construction of a preliminary geological model. This derived geological information should first be assessed for its compatibility with the scale of the problem, and any directly observed geological data. Once verified it can be used to help constrain the preferred geological map interpretation being developed by the field geologist. The method models the contacts as planar surfaces. Therefore, it must be ensured that this assumption fits the scale and geometry of the problem. Two examples are shown from folded sequences at the Bathurst Mining Camp, New Brunswick, Canada.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2010.04a
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• pp.135-138
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• 2010

IMO adopt the revision of SOLAS convention on requirement systems for ECDIS and considered an ECDIS as the major system for E-Navigation strategy on marine transportation safety and environment protection. ENC(Electronic Navigational Chart) as base map of ECDIS is considered as a principal information infrastructure that is essential for navigation tasks. But ENCs are not easy to utilize because they are encoded according to ISO/IEC 8211 file format, and ENCs is required to utilize in parts of Marine GIS and various marine application because they are used for navigational purpose mainly. Meanwhile Google earth is satellite map that Google company service, is utilized in all kinds of industry generally providing local information including satellite image, map, topography, 3D building information, etc. In this paper, we developed KML conversion technology for ENC application. details of development contents consist of ENC loading module and KML conversion module. Also, we applied this conversion technology to Korea ENC and evaluated the results.