인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This study investigates the feasibility of producing high-precision 1:1,000 digital topographic maps using drone imagery in Dong-myeon, Yanggu-gun, a flight-restricted area. The study area covers approximately 1 km × 1 km, with elevations ranging from 273.5 m to 432.5 m and a maximum relief of 159 m. Image acquisition was conducted using a Wingtra One GenⅡ unmanned aerial vehicle, while GCPs (Ground Control Points) were surveyed using the Network RTK (Real-Time Kinematic) method. To reduce reliance on ground control points in restricted-access environments, the PPK (Post-Processed Kinematic) technique was applied, and positional accuracy was evaluated using four GCP configurations consisting of 9, 7, 5, and 3 points. The experimental results showed that all cases met the accuracy requirements for 1:1,000 digital topographic map production, namely horizontal and vertical position RMSE values of less than 0.3 m and maximum errors of less than 0.6 m. In addition, the feasibility of generating contour lines from point clouds derived through image matching was examined. The analysis revealed that accuracy requirements were satisfied in non-forested areas, whereas significant errors occurred in densely forested regions. These findings highlight inherent limitations in forested environments, where supplementary data such as forest standard maps and additional sensors may be required to ensure consistent contour quality. Overall, the results demonstrate both the technical feasibility and limitations of drone- and PPK-based topographic mapping in flight-restricted areas, providing a foundation for future institutional and regulatory developments related to drone-based public surveying.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-151-26-02-096302353
