• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.1
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• pp.1-16
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• 2024

Alvin Toffler mentioned that it is important for future society to keep pace with synchronization and that time deviations can hinder social development. As we experience the new normal era of untact, we have experienced an increase in non-face-to-face contact and accelerated digital transformation. Amid these rapid changes, we can maintain the need for synchronization or change in space. Therefore, we would like to study what kind of settlements people create and choose. We looked at the metaverse as an object that could indirectly find out about this, and used the content called "Animal Crossing" to collect data related to the spatial form of the metaverse. Sampling utilized a judgment sampling method during non-probability sampling to alleviate differences due to the progress of the game. The collected data was classified according to floor plan and location type and briefly organized through descriptive statistics. After matching each facility by use, data was constructed by setting coordinates for each cluster and listing them. This data was interpreted graphically on the coordinate plane for each cluster, and Euclidean analysis was performed to analyze the relationships between clusters and residential choice using a Euclidean matrix. As a result of the analysis, it could be interpreted that efficiency was pursued by arranging similar functions in close proximity. Nevertheless, when choosing a residence, it was interpreted that the intention was to create a community through arrangement adjacent to residents rather than efficiency or convenience. Due to the differences between the metaverse and the real world, it is expected that there will be limitations in equating it with reality. However, through the space expressed in the virtual world by people who are far away from the constraints of reality, we can indirectly know the wishes that we have not been able to express due to our lack of awareness.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1125-1139
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• 2022

In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.