• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.63-71
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• 2012

Recently, web portals have been offering georeferenced SLI(Street-Level Imagery) services, such as Google Streetview. The SLI has a distinctive strength over aerial images or vector maps because it gives us the same view as we see the real world on the street. Based on the characteristic, applicability of the SLI can be increased substantially through conflation with other spatial datasets. However, spatial inconsistency between different datasets is the main reason to decrease the quality of conflation when conflating them. Therefore, this research aims to remove the spatial inconsistency to conflate an SLI with a widely used 2D vector map. The removal of the spatial inconsistency is conducted through three sub-processes of (1) road intersection matching between the SLI trace and the road layer of the vector map for detecting CPPs(Control Point Pairs), (2) inaccurate CPPs filtering by analyzing the trend of the CPPs, and (3) local alignment using accurate CPPs. In addition, we propose an evaluation method suitable for conflation result including an SLI, and verify the effect of the removal of the spatial inconsistency.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.6
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• pp.1-14
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• 2019

Carbon storage is one of the regulating ecosystem services provided by urban street trees. It is important that evaluating the economic value of ecosystem services accurately. The carbon storage of street trees was calculated by measuring the morphological parameter on the field. As the method is labor-intensive and time-consuming for the macro-scale research, remote sensing has been more widely used. The airborne Light Detection And Ranging (LiDAR) is used in obtaining the point clouds data of a densely planted area and extracting individual trees for the carbon storage estimation. However, the LiDAR has limitations such as high cost and complicated operations. In addition, trees change over time they need to be frequently. Therefore, Structure from Motion (SfM) photogrammetry with unmanned Aerial Vehicle (UAV) is a more suitable method for obtaining point clouds data. In this paper, a UAV loaded with a digital camera was employed to take oblique aerial images for generating point cloud of street trees. We extracted the diameter of breast height (DBH) from generated point cloud data to calculate the carbon storage. We compared DBH calculated from UAV data and measured data from the field in the selected area. The calculated DBH was used to estimate the carbon storage of street trees in the study area using a regression model. The results demonstrate the feasibility and effectiveness of applying UAV imagery and SfM technique to the carbon storage estimation of street trees. The technique can contribute to efficiently building inventories of the carbon storage of street trees in urban areas.

• Korean Journal of Artificial Intelligence
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• v.12 no.4
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• pp.1-7
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• 2024

The use of street view has many benefits with its popular source being Google Street View (GSV). One of the processing methods uses semantic segmentation which can classify each pixel according to the category of the pre-trained pyramid scene parsing network (PSPNet) model used. The Green View Index (GVI) is one of the semantic segmentation research trends in viewing Green Open Space (GOS) based on human perception of an area. Green Open Space (GOS) provides many benefits and more attractiveness to the community to be able to live in the vicinity. The GVI obtained gives an average value of 22.5% capturing the presence of GOS which is higher than the green open space data collected by Housing and Settlement Area, Land and Parking Offices Bandung City.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.35-43
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• 2013

Georeferenced SLI(Street-Level Imagery) services such as Google Streetview, which contain abundant information about the real world, can increase its applicability substantially through conflation with other spatial datasets. For this purpose, we propose a method to improve a correspondence of building region to combine building information more accurately. First, the spatial inconsistency between SLI and vector map is removed by alignment based on road intersections. Then, visible building regions are searched from the spatial inconsistency-removed vector map, and the optimal corresponding building areas are determined in the SLI scene using the visible regions as seed information. The experimental results demonstrated that our method had improved the accuracy of building region correspondence by about 8%. Therefore, our method can be utilized effectively for enhancement of conflation service based on the SLI.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.68-80
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• 2015

To increase the utilization of the KOrea Multi-Purpose SATellite(KOMPSAT) series imagery being developed pursuant to the national space development program, high quality images with enhanced locational accuracy should be created through standardized post-processing processes. In the present study, using the Value-Added Processing System(VAPS) constructed for the post-processing of KOMPSAT imagery, location correction experiments were conducted using KOMPSAT-2 and -3 imagery from domestic and overseas regions. First, 50 pieces from each of KOMPSAT-2 imagery were selected from South Korean and North Korean regions, and modeling was conducted using GCP Chips. According to the results, the Root Mean Square Errors(RMSE) for South Korea and North Korea were 1.59 pixels and 2.04 pixels, respectively, and the locational accuracy of ortho mosaic imagery using check points were 1.33m(RMSE) and 1.90m(RMSE), respectively. Meanwhile, in the case of overseas regions for which GCP could not be easily obtained, the improvement of locational accuracy could be identified through image corrections using Open Street Map(OSM). The VAPS and reference materials used in the present study are expected to be very useful in constructing a precise image DB for entire global regions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.4
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• pp.405-412
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• 2012

After the spatial discrepancy between SLI(Street-Level Imagery) and vector map is removed by their conflation, the corresponding building regions can be found based on SLI parameters. The building region correspondence, however, is not perfect even after the conflation. This paper aims to improve the correspondence of building regions by region splitting of an SLI. Regions are initialized by the seed lines, projection of building objects onto SLI scene. First, sky images are generated by filtering, segmentation, and sky region detection. Candidates for split lines are detected by edge detector, and then images are splitted into building regions by optimal split lines based on color difference and sky existence. The experiments demonstrated that the proposed region splitting method had improved the accuracy of building region correspondence from 83.3% to 89.7%. The result can be utilized effectively for enhancement of SLI services.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.1
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• pp.115-126
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• 2018

A new urban paradigm that moves from a vehicle-centric to pedestrian-centric culture should be considered to improve the quality of the pedestrian environments for women, children, senior citizens, and disabled persons as well as to promote community unification by providing general movement rights to everyone. This study was implemented to provide decent alternatives to improve street spaces. The street spaces around the Commercial and Residential Area No.1 located in the Administrative-Centered City, Sejong Special Autonomic City, were selected to analyze and define the status of the walkways and the street spaces. Satellite imagery and numerical maps were used to collect geographic data. Practical and actual surveys for the selected sites were performed to analyze the street status and the pedestrian status. Based on the all collected data, analysis results, and literature reviews, the questionnaire was made, and 315 inquiries qualified for analysis. The physical status of all four study sites was the highest level, Grade A, and green open spaces were relatively sufficient. As a result, the factors obtained from the factor analysis have an impact on the satisfaction of the pedestrian streets in the commercial area. The factors are as followed Design > Convenience > Roadside trees and rest areas > Safety > Safety protective facilities > Transportation and information facilities > Continuity > Basic state of road surfaces > Comfortability, and in the residential area: Transportation and information facilities > Basic state of road surfaces > Comfort > Convenience > Continuity > Design > Illumination and crime prevention facilities > Safety > Roadside trees and rest areas.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.755-767
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• 2014

In this study, detailed air flow characteristics in an urban areas were analyzed using GIS data and a Computational Fluid Dynamics (CFD) model. For this, a building construction algorithm optimized for Geographic Information System (GIS) data with a vector format (Los Angeles region imagery acquisition consortium 2 geographic information system, LARIAC2 GIS) was used. In the LARIAC2 GIS data, building vertices were expressed as latitude and longitude. Using the model buildings constructed by the algorithm as the surface boundary data in the CFD model, we performed numerical simulations for two building-congested areas in Los Angeles using inflow information provided by California Air Resources Board. Comparing with the inflow, there was a marked difference in wind speed and direction within the target areas, which was mainly caused by the secondarily induced local circulations such as street-canyon vortices, horse-shoe vortices, and recirculation zones. In street canyons parallel to the inflow direction, wind speed increased due to a channeling effect and, in street canyons perpendicular to the inflow direction, vertically well developed vortices were induced. In front of a building, a horse-shoe vortex was developed near the surface and, behind a building, a recirculation zone was developed. Near the surface in the areas where the secondarily induced local circulations, wind speed remarkably increased. Overall, wind direction little (largely) changed at the areas where wind speed largely increased (decreased).

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.49-60
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• 2017

Recently, the use of mashup that integrates real-time data with spatial data such as tiled map and satellite imagery has been increased significantly. As the use of mashup has been extended to various fields of O2O, LBS, Smart City, and Autonomous Driving, the performance of mashup has become more important. Therefore, this study aims to compare and analyze the performance of various map platforms, when large amounts of real-time data are integrated with spatial data. Specifically, we compare the performance of most popular map platforms available in Korea, such as Google Maps, OpenStreetMap, Daum Map, Naver Map, olleh Map, and VWorld. We also compare the performance using most common web browsers of Chrome, Firefox and Internet Explorer. In the performance analysis, we measured and compared the initialization time of basic map and the mashup time of real-time data for the above map platforms. From analysis results, we could find that Google Maps, OpenStreetMap, VWorld, and olleh Map platforms showed a better performance than the others.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.143-156
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• 2023

To precisely and reliably analyze the contents of the satellite imagery, recognizing the clouds which are the obstacle to gathering the useful information is essential. In recent times, deep learning yielded satisfactory results in various tasks, so many studies using deep neural networks have been conducted to improve the performance of cloud detection. However, existing methods for cloud detection have the limitation on increasing the performance due to the adopting the network models for semantic image segmentation without modification. To tackle this problem, we introduced the multi-branch neural architecture search to find optimal network structure for cloud detection. Additionally, the proposed method adopts the soft intersection over union (IoU) as loss function to mitigate the disagreement between the loss function and the evaluation metric and uses the various data augmentation methods. The experiments are conducted using the cloud detection dataset acquired by Arirang-3/3A satellite imagery. The experimental results showed that the proposed network which are searched network architecture using cloud dataset is 4% higher than the existing network model which are searched network structure using urban street scenes with regard to the IoU. Also, the experimental results showed that the soft IoU exhibits the best performance on cloud detection among the various loss functions. When comparing the proposed method with the state-of-the-art (SOTA) models in the field of semantic segmentation, the proposed method showed better performance than the SOTA models with regard to the mean IoU and overall accuracy.