• 한국환경복원기술학회지
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• 제10권2호
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• pp.71-83
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• 2007

This study aims to provide basic data for planning the supply and demand of urban parks and setting up facilities in urban parks that reflect the use patterns of visitors. The study was conducted by surveying the total number of urban park users and the number of users for different time zones and analyzing their use patterns through on-site surveys at 17 urban parks located in seven cities/counties in Gyeonggi province. The result showed that as for distribution of urban park users by time zone, the number of users was highest in the afternoon. Between 7 and 9 o'clock in the morning in weekdays, many users came to urban parks for their morning exercises. There was hardly no difference between male and female. When the number of users of 17 urban parks was surveyed, a park was used by 734 people per day on weekdays in average. When the total number of users per year was estimated, a resident nearby a park visited the place 4.7 times a year in average. In addition, the analysis showed that the number of users of man-made parks was twice as high as that of natural parks. The number of users did not vary depending on the size of a park. As for use patterns by time zone, no difference was found in activity types between parks larger than 100,000 $m^2$ and those less than 100,000 $m^2$. Usually, in the morning, users came for exercises In the afternoon, users visited the parks for more diverse purposes. In the evening, they tended to visit parks to work out and wrap up a day. Going forward, the outcome of this study should be utilized as important data for analyzing the demand and supply of urban parks. In addition, the number of users by time zone and their use patterns should be fully considered when introducing facilities in parks.

• 한국측량학회지
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• 제38권3호
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• pp.187-196
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• 2020

본 연구에서는 UAV (Unmanned Aerial Vehicle)와 PlanetScope 위성영상을 함께 이용한 붕괴건물 탐지를 수행하여 지표면에 위치한 특정 객체 탐지에 있어 이종 센서의 활용 가능성을 제시하였다. 이를 위해 지난해 4월 산불 피해로 붕괴된 20여 채의 건물들이 있는 곳을 실험장소로 선정하였다. 붕괴건물 탐지를 위해 1차적으로 객체기반 분할을 수행한 고해상도의 UAV 영상을 이용해 ExG (Excess Green), GLCM (Gray-Level Co-occurrence Matrix) 그리고 DSM (Digital Surface Model)과 같은 객체들의 특징(feature) 정보를 생성한 후 이를 붕괴건물 후보군 탐지에 이용하였다. 이 과정에서 탐지정확도 향상을 위해 PlanetScope를 이용한 변화탐지 결과를 함께 사용하였으며 이를 시드 화소(seed pixles)로 사용하여 붕괴건물 후보군에서 오탐지된 영역과 과탐지된 영역을 수정 및 보완하였다. 최종적인 탐지 결과는 참조 영상을 통해 그 성능을 분석하였으며 UAV 영상만을 이용한 붕괴건물 후보군 탐지 결과와 UAV 그리고 PlanetScope 영상을 함께 사용했을 때의 결과의 정확도를 비교, 분석하였다. 그 결과 UAV 영상만을 이용해 탐지한 붕괴건물의 정확도는 0.4867 F1-score를 가지며 UAV와 PlanetScope 영상을 함께 사용했을 때의 결과는 0.8064 F1-score로 그 값이 상승하였다. Kappa 지수 또한 0.3674에서 0.8225로 향상된 것을 확인할 수 있었다.