• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.271-285
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• 2009

Multi-temporal satellite imagery can be changed into a transform image that emphasizes the changed area only through the application of various change detection techniques. From the transform image, an automated change detection model calculates the optimal threshold value for classifying the changed and unchanged areas. However, the model can cause undesirable results when the histogram of the transform image is unbalanced. This is because the model uses a single threshold value in which the sign is either positive or negative and its value is constant (e.g. -1, 1), regardless of the imbalance between changed pixels. This paper proposes an advanced method that can improve accuracy by applying separate threshold values according to the increased or decreased range of the changed pixels. It applies multiple threshold values based on the cumulative producer's and user's accuracies in the automated binary change detection model, and the analyst can automatically extract more accurate optimal threshold values. Multi-temporal IKONOS satellite imagery for the Daejeon area was used to test the proposed method. A total of 16 transformation results were applied to the two study sites, and optimal threshold values were determined using accuracy assessment curves. The experiment showed that the accuracy of most transform images is improved by applying multiple threshold values. The proposed method is expected to be used in various study fields, such as detection of illegal urban building, detection of the damaged area in a disaster, etc.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.37-44
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• 2019

In this study, we propose the automatic extraction method of Rescue Requests from Drone Images. A central object is extracted from each image by using central object extraction method[7] before classification. A central object in an images are defined as a set of regions that is lined around center of the image and has significant texture distribution against its surrounding. In this case of artificial objects, edge of straight line is often found, and texture is regular and directive. However, natural object's case is not. Such characteristics are extracted using Edge direction histogram energy and texture Gabor energy. The Edge direction histogram energy calculated based on the direction of only non-circular edges. The texture Gabor energy is calculated based on the 24-dimension Gebor filter bank. Maximum and minimum energy along direction in Gabor filter dictionary is selected. Finally, the extracted rescue requestor object areas using the dominant features of the objects. Through experiments, we obtain accuracy of more than 75% for extraction method using each features.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.201-205
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• 2007

In the post-Cold War era, acquisition technique of high-resolution satellite imagery (HRSI) has begun to commercialize. IKONOS-2 satellite imaging data is supplied for the first time in the 21st century. Many researchers testified mapping possibility of the HRSI data instead of aerial photography. It is easy to renew and automate a topographical map because HRSI not only can be more taken widely and periodically than aerial photography, but also can be directly supplied as digital image. In this study matching size of IKONOS Geo-level stereo image is presented lot production of digital elevation model (DEM). We applied area based matching method using correlation coefficient of pixel brightness value between the two images. After matching line (where "matching line" implies straight line that is approximated to complex non-linear epipolar geometry) is established by exterior orientation parameters (EOPs) to minimize search area, the matching is tarried out based on this line. The experiment on matching size is performed according to land cover property, which is divided off into four areas (water, urban land, forest land and agricultural land). In each of the test areas, window size for the highest correlation coefficient is selected as propel size for matching. As the results of experiment, the proper size was selected as $123{\times}123$ pixels window, $13{\times}13$ pixels window, $129{\times}129$ pixels window and $81{\times}81$ pixels window in the water area, urban land, forest land and agricultural land, respectively. Of course, determination of the matching size by the correlation coefficient may be not absolute appraisal method. Optimum matching size using the geometric accuracy therefore, will be presented by the further work.

• Korean Journal of Environment and Ecology
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• v.31 no.4
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• pp.420-427
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• 2017

The purpose of this study was to investigate the indirect effects of forest using the forest healing factors such as landscape and hearing factors on mood improvement. The experiment was conducted for about 2 months from October 5, 2016 to November 30, 2016 targeting 30 healthy college students ($23.6{\pm}1.7$ years old). After making 3 factors(image factor, sound factor and image+sound factor) using scenery and sound of both forest and urban space, participants undergone the test in a room. And the effects of these 3 factors on the mood improvement were compared and analyzed using SPSS 18.0 program. Profile of Mood State test (POMS) and Semantic Differential method (SD) were used to measure mood improvement as a psychological test. As a result, indirect forest stimulation showed effects of suppressing tension, fatigue, anger, confusion, depression, and enhancement of vitality. No significant difference was observed in the comparison between forest stimuli. However, Compared with the urban stimuli, the stimulation of the forests has the effect of enhancing pleasant, natural feeling and calmness.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.1
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• pp.16-27
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• 2010

A street, as a linear factor constituting the city, is an axis of urban development. The substantial function of the street is the traffic space for the passage but now it plays the important role of the place of community where contains various activities such as meeting between people and people, rest, entertainment etc. A street is basically the 3 dimensional space consisted of the sidewalk, roadway and the roadside structures surrounding the street. In this case, the roadside structures are the physical composition factors for the street space and the facade of the roadside structures acts as important variables to form the image of street space. Thus, this study is to provide the basic data to be applied in the future urban street landscape plan by extracting the superior factors in visual and perceptional aspects which affect the image of street view from the shape composition factors which constitute the facade of the roadside structures, and by searching and analyzing the satisfaction degree and preference of each factors.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.4
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• pp.365-376
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• 2016

Population of urban areas is rapidly increased due to urbanization. This situation leads to lack of surface space. So, underground space has been developed for resolving the problem of congested urban areas. Many studies have researched for this situation. However, previous studies mainly focused on behaviour of structures. Researches about behaviour of soil are lacked. For this reason, this study has investigated interactive behaviour between embedded pile and its surrounding ground due to tunnelling. Soil deformation is observed by the close range photogrammetric method and image processing in the model test. These data are compared with numerical analysis.

• International Journal of Contents
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• v.6 no.4
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• pp.14-21
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• 2010

In this study, the preferences with regard to floral design in a public space and the correlation between such preferences and the image features of the floral design types were identified. Based on the review of the related literature, the floral designs placed in a public space were classified into three types(the flower bed, container, and three-dimensional sculpture types) according to physical factors, and were classified into two types(the indoor and outdoor types) according to environmental factors. In the floral-design type classified by physical and environmental factors, the flower bed and outdoor types, respectively, were highly preferred. The factor that was found to have the greatest influence on the preference for the flower bed type was 'well-orderedness', which included the adjective categories 'harmonious', 'clean', and 'simple'; that which was found to have the greatest influence on the preference for the outdoor-type floral design was 'identity', which included the adjective categories 'symbolic', 'distinctive', 'harmonious', 'impressive', and 'clean'. It can thus be concluded that the plants that are effectively displayed in a public space can express the nature and identity of the city itself, can be a yardstick for giving value to the city and for evaluating it, and can be important components of the urban landscape. A design that is suitable for the purpose of each public space and that reflects the factors that exert an influence on the users' floral-design type preference should thus be developed.

• Journal of Urban Science
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• v.7 no.2
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• pp.53-60
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• 2018

Digital Twin is a technology that creates a photocopy of real-world objects on a computer and analyzes the past and present operational status by fusing the structure, context, and operation of various physical systems with property information, and predicts the future society's countermeasures. In particular, 3D rendering technology (UAS, LiDAR, GNSS, etc.) is a core technology in digital twin. so, the research and application are actively performed in the industry in recent years. However, UAS (Unmanned Aerial System) and LiDAR (Light Detection And Ranging) have to be solved by compensating blind spot which is not reconstructed according to the object shape. In addition, the terrestrial LiDAR can acquire the point cloud of the object more precisely and quickly at a short distance, but a blind spot is generated at the upper part of the object, thereby imposing restrictions on the forward digital twin modeling. The UAS is capable of modeling a specific range of objects with high accuracy by using high resolution images at low altitudes, and has the advantage of generating a high density point group based on SfM (Structure-from-Motion) image analysis technology. However, It is relatively far from the target LiDAR than the terrestrial LiDAR, and it takes time to analyze the image. In particular, it is necessary to reduce the accuracy of the side part and compensate the blind spot. By re-optimizing it after fusion with UAS and Terrestrial LiDAR, the residual error of each modeling method was compensated and the mutual correction result was obtained. The accuracy of fusion-based 3D model is less than 1cm and it is expected to be useful for digital twin construction.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1294-1294
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• 2024

Flood risk maps are used in urban flooding to understand the spatial extent and depth of inundation damage. To construct these maps, hydrodynamic modeling capable of simulating flood waves is necessary. Flood waves are typically fast, and inundation patterns can significantly vary depending on the terrain, making it essential to accurately represent the terrain of the flood source in flood wave analysis. Recently, methods using UAVs for terrain data construction through Structure-from-Motion or LiDAR have been utilized. These methods are crucial for UAV operations, and thus, still require a lot of time and manpower, and are limited when UAV operations are not possible. Therefore, for efficient nationwide monitoring, this study developed a model that can automatically generate terrain data by estimating depth information from a single image using c-GAN (Conditional Generative Adversarial Networks) and BBDM (Brownian Bridge Diffusion Model). The training, utilization, and validation datasets employed images from the ISPRS (2018) and directly aerial photographed image sets from five locations in the territory of the Republic of Korea. Compared to the ground truth of the test data set, it is considered sufficiently usable as terrain data for flood wave analysis, capable of generating highly accurate and precise terrain data with high reproducibility.