• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.451-456
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• 2006

Shoreline changes its shape and attribution dynamically by natural, unnatural acts and is the most important information for defining a countries territory. These shorelines can apply to frame work of MGIS, and they are getting important because we can implement the data for creating monitoring systems around coastal areas. This study proposed an algorithm for extracting shorelines automatically using a new developed Lidar data which is applied in ocean and coastal areas. Its result was compared to shorelines which were derived from ground survey. It showed stable shorelines in both natural, and artificial coast areas. It showed the possibility of extracting shorelines with LiDAR data and proved the method was more efficient and economical compared to recent studies and methods.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.3-12
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• 2010

In previous studies, the digital measurement systems and analysis algorithms were developed by using the related techniques, such as the aerial photograph detection and high resolution satellite image process. However, these studies were limited in 2-dimensional geo-processing. Therefore, it is necessary to apply the 3-dimensional spatial information and coordinate system for higher accuracy in recognizing and locating of geo-features. The objective of this study was to develop a stochastic algorithm for the optimal sensor placement using the 3-dimensional spatial analysis method. The 3-dimensional information of the LiDAR was applied in the sensor field algorithm based on 2- and/or 3-dimensional gridded points. This study was conducted with three case studies using the optimal sensor placement algorithms; the first case was based on 2-dimensional space without obstacles(2D-non obstacles), the second case was based on 2-dimensional space with obstacles(2D-obstacles), and lastly, the third case was based on 3-dimensional space with obstacles(3D-obstacles). Finally, this study suggested the methodology for the optimal sensor placement - especially, for ground-settled sensors - using the LiDAR data, and it showed the possibility of algorithm application in the information collection using sensors.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.543-551
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• 2006

Recently efforts to extract information about forests by using remote sensing techniques for efficient forest management have progressed actively. In terms of extraction of tree information using single remote sensing data, however, the accuracy of tree recognition and the quantity of extracted information is limited. The objective of this study is to carry out tree modeling in domestic environment applying the latest core technique for tree modeling using color aerial photographs and LiDAR data and to estimate the result of tree modeling. A small-scale coniferous forest was investigated in Daejeon. It was 0.77 that the $R^2$ of accuracy test of tree numbers that estimated with color aerial photography and LiDAR data. In terms of tree height, there was no difference between the estimated value and the field measurements in the case of the group accuracy test of the recently unchanged area. Moreover $R^2$ was 0.83 in the case of the individual accuracy test.

• Spatial Information Research
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• v.18 no.5
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• pp.13-26
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• 2010

In this paper, a dynamic variable window-based topographical classification method is proposed which has the changeable classification units depending on topographical properties. In the proposed scheme, to im prove the classification efficiency, the unit of topographical classification can be changeable dynamically according to the topographical properties and repeated patterns. Also, in this paper, the classification efficiency and accuracy of the proposed method are analyzed in order to find an optimal maximum decision window-size through the experiment. According to the experiment results, the proposed dynamic variable window-based topographical classification method maintains similar accuracy but remarkably reduce computing time than that of a fixed window-size based one, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.29-38
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• 2010

The airborne LiDAR which was introduced in the early 2000's provides the point data. The new methods for the verification of LiDAR materials with high accuracy which is different from the existing airborne survey are needed. In accordance with the rules of airborne laser survey which were enacted in 2009, the verifications by three methods of Unmeasured Rate and point accuracy, point density have been executed, and Unmeasured Rate is to evaluate the rate for the presence of points within uniform grids except non-reflective areas such as watershed areas. For the calculation of Unmeasured Rate, non-reflective areas should be removed by all means, and in case of normal LiDAR materials, as there are scant points for watershed areas, watershed areas should be divided by additional spatial information. So, in this study, the watershed areas were extracted using domain extension technique from the high resolution CIR images of 0.3m grade. In addition, in order to compare the accuracy of Unmeasured Rate calculated, the comparative analysis of the Unmeasured Rate calculated by digital maps has been done. In conclusion, we found that 1I1e accuracy of Unmeasured Rate extracted by domain extension technique is similar to the value extracted by digitizing technique.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.485-487
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• 2009

The Spatial Image contents of Geomorphology 3-D environment is focused by the requirement and importance in the fields such as, national land development plan, telecommunication facility management, railway construction, general construction engineering, Ubiquitous city development, safety and disaster prevention engineering. The currently used DEM system using contour lines, which embodies geographic information based on the 2-D digital maps and facility information has limitation in implementation in reproducing the 3-D spatial city. Moreover, this method often neglects the altitude of the rail way infrastructure which has narrow width and long length. This As the results, We confirmed the solutions of varieties application for railway facilities management using 3-D spatial image contents and database design. Also, I suggested that U-city using topographical modeling about matching methods of high density elevation value using 3-D aerial photo with laser data are best approach for detail stereo modeling and simulation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.4
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• pp.335-341
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• 2006

This paper aims at developing an efficient method that extracts building using local spatial association of raw LiDAR data without setting up empirical variables such as a minimum building area, and applying the method to survey data to evaluate the efficiency of that. To do this, LISA(Local Indicatiors of Spatial Association) statistics are used which reflect local variations that can be appeared in the research area. It can be also a preprocess that detects spatial outliers through the significance test of LISA statistics and interpolate using kernel estimation. Boundaries of buildings as well as buildings can be extracted based on quadrant of Moran Scatterplot. Experimental results show that the proposed method is promising in extracting buildings from LiDAR data automatically.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.148-157
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• 2012

Recently, people are getting more interested in green energy resource and environment friendly energy resource due to the lack of energy and global warming. This study produced a solar energy resource map using LiDAR(Light Detection And Ranging) data to check if it is utilized for spatial information technology and solar energy sectors that people pay more attentions to as new recycling energy. This study assigned Ulleungdo(Island) located in Gyeongsangbuk-do as a target area. This study created the contour line with 1 meter by newly photographing LiDAR and data processing. And using this contour line, this study built DEM(Digital Elevation Model) data with 1 meter. The incidence range depending on the altitude and azimuth of sun using DEM data is used to evaluate solar energy resource. This is expected to suggest an accurate method to evaluate more reliable and more precise information of new recycling energy resource by producing solar energy resource map based on accurate and precise spatial resolution data with 1 meter level.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.3
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• pp.166-173
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• 2008

Recently, problem of earth environment being attended with international issue, people are concerned about the environmentally-friendly and renewable biomass energy. Especially, the forest biomass is more important because Korea have to control carbon footprint for Kyoto Protocol and Convention on Climate Change. In case of Korea, forest area covers the land about 2/3 of all country. It is needed that more economical and efficient method to estimate the biomass by remote sensing data which include wide coverage and is progressed by one-step. In this study, we estimate forest biomass with LiDAR data and aerial photograph. Three biomass equation is used and estimate mean biomass of single tree and entire biomass in plots. The results are compared with field data. $R^2$ of the mean biomass of single tree is greater than 0.8 and that of entire biomass in plots is greater than 0.65. In conclusion, the method using remote sensing data is verified more economical and efficient than previous field data method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.635-644
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• 2020

Object recognition, detection and instance segmentation based on DL (Deep Learning) have being used in various practices, and mainly optical images are used as training data for DL models. The major objective of this paper is object segmentation and building detection by utilizing multimodal datasets as well as optical images for training Detectron2 model that is one of the improved R-CNN (Region-based Convolutional Neural Network). For the implementation, infrared aerial images, LiDAR data, and edges from the images, and Haralick features, that are representing statistical texture information, from LiDAR (Light Detection And Ranging) data were generated. The performance of the DL models depends on not only on the amount and characteristics of the training data, but also on the fusion method especially for the multimodal data. The results of segmenting objects and detecting buildings by applying hybrid fusion - which is a mixed method of early fusion and late fusion - results in a 32.65% improvement in building detection rate compared to training by optical image only. The experiments demonstrated complementary effect of the training multimodal data having unique characteristics and fusion strategy.