• Journal of the Society of Disaster Information
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• v.13 no.3
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• pp.296-304
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• 2017

In order to predict about forest fire behavior we constructed a database for combustion characteristic of forest fuels in Samcheok, Gangwon-do and prepared fire risk map and fire risk rating using GIS method in this study. For the mapping autoignition temperature, ignition time, flame duration time, total heat release and total smoke release are selected as the standardized parameters and the overall risk rating was made up of the ignition risk parameters(autoignition temperature, ignition time) and the spread risk parameters(flame duration time, total heat release, total smoke release). Forest fire risk was classified into 5 grades and lower grade of fire risk rating mean to correspond to more dangerous forest fire. As a result, the overall risk rating of Samcheok was classified into three grades from 1 to 3 and Nogok-myeon and Miro-myeon were turned out the most dangerous areas for forest fire. Because of the colony of pine and oak trees and the higher fire loads, the flame propagation will be carried out quickly in these areas.

• Journal of the Korean GEO-environmental Society
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• v.20 no.5
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• pp.5-12
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• 2019

On November 15, 2017, a unpredictable liquefaction damage was occurred at the $M_L=5.4$ Pohang earthquake and after, many researches have been conducted in Korea. In Korea, where there were no cases of earthquake damage, it has been extremely neglectable in preparing earthquake risk maps and building earthquake systems that corresponded to prevention and preparation. Since it is almost impossible to observe signs and symptoms of drought, floods, and typhoons in advance, it is very effective to predict the impacts and magnitudes of seismic events. In this study, 14,040 borehole data were collected in the metropolitan area and liquefaction evaluation was performed using the amplification factor. Based on this data, liquefaction hazard maps were prepared for ground accelerations of 0.06 g, 0.14 g, 0.22 g, and 0.30 g, including 200years return period to 4,800years return period. Also, the correlation analysis between the earthquake acceleration and LPI was carried out to draw a real-time predictable liquefaction hazard map. As a result, 707 correlation equations in every cells in GIS map were proposed. Finally, the simulation for liquefaction risk mapping against artificial earthquake was performed in the metropolitan area using the proposed correlation equations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.267-277
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• 2019

Photogrammetry and computer vision are identical in determining the three-dimensional coordinates of images taken with a camera, but the two fields are not directly compatible with each other due to differences in camera lens distortion modeling methods and camera coordinate systems. In general, data processing of drone images is performed by bundle block adjustments using computer vision-based software, and then the plotting of the image is performed by photogrammetry-based software for mapping. In this case, we are faced with the problem of converting the model of camera lens distortions into the formula used in photogrammetry. Therefore, this study described the differences between the coordinate systems and lens distortion models used in photogrammetry and computer vision, and proposed a methodology for converting them. In order to verify the conversion formula of the camera lens distortion models, first, lens distortions were added to the virtual coordinates without lens distortions by using the computer vision-based lens distortion models. Then, the distortion coefficients were determined using photogrammetry-based lens distortion models, and the lens distortions were removed from the photo coordinates and compared with the virtual coordinates without the original distortions. The results showed that the root mean square distance was good within 0.5 pixels. In addition, epipolar images were generated to determine the accuracy by applying lens distortion coefficients for photogrammetry. The calculated root mean square error of y-parallax was found to be within 0.3 pixels.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• Spatial Information Research
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• v.13 no.3 s.34
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• pp.297-307
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• 2005

This study was performed to get the basic data for the dredging project and logical maintenance of the Yaedang reservoir. The survey of reservoir capacity for calculation of sediment volume carried out using DGPS Echo-Sounder during November $25\~30$ in 2004. The latitude and longitude signal from GPS satellite was received a second interval with the UTM coordinate system. Water depth was measured 0.2 second interval by Echo-sounder sensor in MIDAS Surveyor. The UTM coordinate datum were transformed into standard coordinate datum of Korean(TM coordinate datum) using Arc Info System. Mapping of contour was used Sufer, Arc View and Auto CAB Program Storage capacity of Yaedang reservoir was estimated by average contour area method. Result of this time investigation for useful storage determination of Yaedang reservoir was showed 4,601.585 ha-m and was differenced less 5.425ha-m the bygones useful storage.

• Korean Journal of Environment and Ecology
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• v.22 no.4
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• pp.347-355
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• 2008

This study analyzed and modeled the relationships between the species richness of fish, plant, and bird and environmental factors such as climatic and geographical variables based on data collected from 109 major drainage systems in Japan from 1990 until 2005. As a result, the most parts of the distributions of the fish, plant, and bird species richness were clarified by the average annual atmospheric temperature, dimension of drainage areas, and annual rainfall, respectively. In addition, this study predicted the value of each organism species distributed in national drainage areas in Japan using GAMs(Generalized Additive Models) for each organism model created by environmental factors on a wide-ranging scale, and also mapped out the value. Mapping out the predicted value could make it easier for its managers to newly set up the areas needing to be protected to obtain diversity of the organism species and to assess their availability of conservation for bio-diversity.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.2
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• pp.11-23
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• 2000

The subjects of this study are to examine and to apply the methods of making 1 : 10,000 scale digital maps using Russian's 2 m resolution satellite images of Alternative and 8 m resolution stereo satellite images of MK-4 for the Kyoha area of Paju-city where aerial-photo surveying is not possible. A digital elevation model (DEM) was calculated from MK-4 images. With this DEM, the Alternative images were orthorectified. Ground control points (GCP) were acquired from GPS surveyings and were used to perform geometric corrections on Alternative images. From field investigation, thematic attributes are digitized on the monitor. RMS errors of the planar and vertical positions are estimated to ${\pm}0.4$ m and ${\pm}15$ m, respectively. The planar accuracy is better than an accuracy required by NGIS (national GIS) programs. Local information from field investigation was added and the resulting maps should be good as base maps for, such as, regional and urban plannings.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.211-224
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• 2016

Based on the 'Government 3.0', the government opens the public data and encourages the active use in the private sector. Recently, the spatial and statistical information that is one of the public data is being widely used in the various web business as a high value-added information. In this study, we propose an architecture of high-availability, high-reliability and high-performance open platform which can provide a variety of services such as searching, analysis, data mining, and thematic mapping. In particular, we present two different system architectures for the government and the public services, by reflecting the importance of the information security and the respective utilization in the private and public sectors. We also compared a variety of server architecture configurations such as a clustered server configuration, a cloud-based virtual server configuration, and a CDN server configuration, in order to design a cost- and performance-effective spatial-statistical information open platform.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.423-436
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• 2017

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.15 no.1
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• pp.117-129
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• 1997

In recent years, the 1 : 5,000 scale Digital National Basemap(DNB) has been generated under National Geo-graphic Information System(NGIS) Project. The DNB database generated will be the backdrop data for thematic maps, underground facilities maps and so on. The DNB database will be distributed to the government and private sectors in near future so that it should meet the requirements as the basic data. In order to assure the quality of DNB database, the establishment of quality assurance process to database was in great need. In this study, we were mainly concerned with improving the quality of digital national basemap database in geomatric aspect as well as the processing time due to the amount of digital data generated. As a results of this study, the quality assuance process of DNB database is established and automatic quality assurance program is developed. Also, the program developed in this study is contributed to quality assurance of DNB database as well as economic aspects.