• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.19 no.3
• /
• pp.229-236
• /
• 2001

One of the basic tasks in hydrological analysis is to delineate drainage basins and channel networks. Characteristics of channel networks and drainage basin parameters have been used widely in hydrologic calculation and modeling. DEMs(Digital Elevation Models) are generally used to automatically map the channel networks and to delineate drainage basins. This paper presents an effort to analyze basin characteristics using various DEMs. The quantitative analysis of channel networks begins with Horton's method of classifying stream according to Horton orders in hydro-geomorphy. HGSIS(Hydro-Geo-Spatial Information System) is possible to extract parameters. Usually, hydrologists. surveyors and GSIS researchers have some difficulties in accessing satellite images and in extracting DEMs from them. Therefore, the extracted DEMs from contours of digital map is widely used to have the basic works of hydrological analysis. This study presents proper DEMs to calculate Horton's orders, width function, drainage area, main channel length, total channel length, basin elevation and basin slope at digital map of 1:25,000 scale.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2008.05a
• /
• pp.1378-1383
• /
• 2008

댐 저수지에서 지속적인 탁도를 유발하는 물질은 쉽게 침강되지 않는 $20{\mu}m$이하의 작은 부유물질(SS)이며, 가을 수직혼합 시기까지 침강되지 않은 부유물질은 다시 재부상하는 경우도 발생한다. 저수지내 탁수의 장기 체류는 수자원 이용과 하류하천의 수생태계에 다양한 문제를 야기하고 있어 일부 댐에서는 실시간 탁도 감시 장치를 설치하고 취수설비를 개선하는 등의 탁수저감 대책의 노력을 기울이고 있으나, 시설의 최적 운영을 지원할 수 있는 탁수 거동 및 탁도 예측에 관한 연구는 아직 부족한 실정이다. 특히, 탁도는 물 속에 존재하는 부유물질의 광학적 특성(light attenuation)을 나타내는 지표로써 SS와는 물리적인 물성이 달라 실시간 계측자료(탁도)와 모델의 모의 변수(SS)가 다른 문제점 때문에 모델링에 어려움이 있었다. 지금까지 탁도 모델링은 대부분 탁도와 SS의 상관관계를 이용하는 방법을 사용하였다. 그러나 이 방법은 탁도-SS 관계가 실측지점과 입자크기분포에 따라 달라지는 특성 때문에 변환과정에 예측결과의 불확실성이 내재한다는 지적을 받아왔다. 본 연구의 목적은 저수지로 유입한 탁수의 보다 과학적이고 정확한 탁도 예측을 위해 탁도를 유발하는 부유물질의 입자크기 분포와 공간적으로 변하는 탁도-SS의 상관관계를 고려할 수 있는 표준화된 탁도 모델링 방법을 개발하고, 실측자료를 사용하여 제시된 탁도 모델링 방법의 예측 성능을 평가하는데 있다. 부유물질의 이송-확산-침강 모델은 2차원 횡방향 평균 수리 모델과 연결(coupling)되어 수행되며, 저수지 수면을 통한 열 교환, 바람과 바닥 조도에 의한 난류혼합과 성층해석, 하천 유입수의 저수지내 밀도류 유동, 그리고 입자 크기별 부유물질의 독립침강을 해석한다. 부유입자의 크기분포와 공간적으로 서로 다른 탁도-SS 관계를 고려한 탁도 예측모델은 기존의 탁도를 종속변수로 사용한 예측 방법 또는 단일 입자크기를 사용한 모델보다 개선된 모의결과를 보여주었다. 본 연구에서 제시된 탁도 예측 알고리즘은 실시간 탁수감시와 예측 모델링, 그리고 댐 방류수 탁도 관리를 위한 선택취수 설비의 운영을 위한 의사결정지원시스템에 적용 가능할 것으로 사료된다.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.3
• /
• pp.587-593
• /
• 2019

The field of geospatial information is rapidly changing due to the development of sensor and data processing technology that can acquire location information. And demand is increasing in various related industries and social activities. The construction and utilization of three dimensional geospatial information that is easy to understand and easy to understand can be an essential element to improve the quality and reliability of related services. In recent years, 3D laser scanners are widely used as 3D geospatial information construction technology. However, 3D laser scanners may cause shadow areas where data acquisition is not possible when objects are large in size or complex in shape. In this study, 3D model of an object has been created by acquiring oblique images using an unmanned aerial vehicle and processing the data. The study area was selected, oblique images were acquired using an unmanned aerial vehicle, and point cloud type 3D model with 0.02 m spacing was created through data processing. The accuracy of the 3D model was 0.19m and the average was 0.11m. In the future, if accuracy is evaluated according to shooting and data processing methods, and 3D model construction and accuracy evaluation and analysis according to camera types are performed, the accuracy of the 3D model will be improved. In the point cloud type 3D model, Cross section generation, drawing of objects, and so on, it is possible to improve work efficiency of spatial information service and related work.

• Journal of Broadcast Engineering
• /
• v.20 no.4
• /
• pp.557-567
• /
• 2015

Over the past decades, a variety of spatial saliency methods have been introduced. Recently, motion saliency has gained much interests, where motion data estimated from an image sequence are utilized. In general, motion saliency requires reliable motion data as well as image segmentation for producing satisfactory saliency map which poses difficulty in most natural images. To overcome this, we propose a motion-based saliency generation that enhances the spatial saliency based on the combination of spatial and motion saliencies as well as motion complexity without the consideration of complex motion classification and image segmentation. Further, an affinity model is integrated for the purpose of connecting close-by pixels with different colors and obtaining a similar saliency. In experiment, we performed the proposed method on eleven test sets. From the objective performance evaluation, we validated that the proposed method produces better result than spatial saliency based on objective evaluation as well as ROC test.

• Journal of Korean Society for Geospatial Information Science
• /
• v.17 no.1
• /
• pp.21-35
• /
• 2009

KOMPSAT-2 is the seventh high-resolution image satellite in the world that provides both 1m-grade panchromatic images of the GSD and 4m-grade multispectral images of the GSD. It's anticipated to be used across many different areas including mapping, territory monitoring and environmental watch. However, due to the complexity and security concern involved with the use of the MSC, the use of KOMPSAT-2 images are limited in terms of geometric images, such as satellite orbits and detailed mapping information. Therefore, this study aims to produce DEM and orthoimage by using the stereo images of KOMPSAT-2, and to explore the applicability of geo-spatial information with KOMPSAT -2. Orientation interpretations were essential for the production of DEM and orthoimage using KOMPSAT-2 images. In the study, they are performed by utilizing both RPC and GCP. In this study, the orientation interpretations are followed by the generation of DEM and orthoimage, and the analysis of their accuracy based on a 1:5,000 digital map. The accuracy analysis of DEM is performed and the results indicate that their altitudes are, in general, higher than those obtained from the digital map. The altitude discrepancies on plains, hills and mountains are calculated as 1.8m, 7.2m, and 11.9m, respectively. In this study, the mean differences between horizontal position between the orthoimage data and the digital map data are found to be ${\pm}3.081m$, which is in the range of ${\pm}3.5m$, within the permitted limit of a 1:5,000 digital map. KOMPSAT-2 images are used to produce DEM and orthoimage in this research. The results suggest that DEM can be adequately used to produce digital maps under 1:5,000 scale.

• Journal of KIISE:Software and Applications
• /
• v.36 no.6
• /
• pp.479-490
• /
• 2009

A number of temporal abstraction approaches have been suggested so far to handle the high computational complexity of Markov decision problems (MDPs). Although the structure of temporal abstraction can significantly affect the efficiency of solving the MDP, to our knowledge none of current temporal abstraction approaches explicitly consider the relationship between topology and efficiency. In this paper, we first show that a topological measurement from complex network literature, mean geodesic distance, can reflect the efficiency of solving MDP. Based on this, we build an incremental method to systematically build temporal abstractions using a network model that guarantees a small mean geodesic distance. We test our algorithm on a realistic 3D game environment, and experimental results show that our model has subpolynomial growth of mean geodesic distance according to problem size, which enables efficient solving of resulting MDP.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.20 no.4
• /
• pp.89-101
• /
• 2017

Crime occurs differently based on not only place locations and building uses but also the characteristics of the people who use the place and the spatial structures of the buildings and locations. Therefore, if spatial big data, which contain spatial and regional properties, can be utilized, proper crime prevention measures can be enacted. Recently, with the advent of big data and the revolutionary intelligent information era, predictive policing has emerged as a new paradigm for police activities. Based on 7420 actual crime incidents occurring over three years in a typical provincial city, "J city," this study identified the areas in which crimes occurred and predicted risky areas. Spatial regression analysis was performed using spatial big data about only physical and environmental variables. Based on the results, using the street width, average number of building floors, building coverage ratio, the type of use of the first floor (Type II neighborhood living facility, commercial facility, pleasure use, or residential use), this study established a Crime Incident Prediction Model (CIPM) based on Bayesian probability theory. As a result, it was found that the model was suitable for crime prediction because the overlap analysis with the actual crime areas and the receiver operating characteristic curve (Roc curve), which evaluated the accuracy of the model, showed an area under the curve (AUC) value of 0.8. It was also found that a block where the commercial and entertainment facilities were concentrated, a block where the number of building floors is high, and a block where the commercial, entertainment, residential facilities are mixed are high-risk areas. This study provides a meaningful step forward to the development of a crime prediction model, unlike previous studies that explored the spatial distribution of crime and the factors influencing crime occurrence.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.1
• /
• pp.86-99
• /
• 2021

One of the methods to alleviate traffic congestion is to increase the efficiency of the roads by providing traffic condition information on road user and distributing the traffic. For this, reliability must be guaranteed, and quantitative real-time traffic speed prediction is essential. In this study, and based on analysis of traffic speed related to traffic conditions, historical data correlated with traffic flow were used as input. We developed an LSTM model that predicts speed in response to normal traffic conditions, along with a CNN-LSTM model that predicts speed in response to incidents. Through these models, we try to predict traffic speeds during the hour in five-minute intervals. As a result, predictions had an average error rate of 7.43km/h for normal traffic flows, and an error rate of 7.66km/h for traffic incident flows when there was an incident.

• Spatial Information Research
• /
• v.22 no.2
• /
• pp.89-97
• /
• 2014

This study investigated how much the spread of traffic control information affect the traffic congestion in order to identify the behavior of the individual drivers that impacts on the entire transport system. For this purpose, agent-based transportation model was constructed. GIS data were directly used for the transportation model and the processing steps of the simulation results are presented. The results showed that the average speed was not lowered when the traffic information was provided to 30 to 70% of total drivers. In contrast, the driver's average speed is reduced when he traffic information was provided to less than 20% or 80% or more. In summary, the provision of traffic information to drivers has an influence on the traffic flow and bypassing vehicles can generate local congestion. This results can be used as a basis for the future direction of road transport policy.

• Tunnel and Underground Space
• /
• v.32 no.6
• /
• pp.586-597
• /
• 2022

In this study, a model was developed for the estimating the locational thickness information of the upper clay layer to be used for the consolidation vulnerability evaluation in the Nakdong river estuary. To estimate ground layer thickness information, we developed four spatial estimation models using machine learning algorithms, which are RF (Random Forest), SVR (Support Vector Regression) and GPR (Gaussian Process Regression), and geostatistical technique such as Ordinary Kriging. Among the 4,712 borehole data in the study area collected for model development, 2,948 borehole data with an upper clay layer were used, and Pearson correlation coefficient and mean squared error were used to quantitatively evaluate the performance of the developed models. In addition, for qualitative evaluation, each model was used throughout the study area to estimate the information of the upper clay layer, and the thickness distribution characteristics of it were compared with each other.