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    초경량무인비행장치 광학영상 및 LiDAR 기반 토공량 산정 결과 비교 = Comparison of Earthwork Volume Estimates Based on Optical Imagery and LiDAR Data from an Ultra-Light Unmanned Aerial Vehicle

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    https://www.riss.kr/link?id=T17545866

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    This study evaluates the reliability and practical efficiency of earthwork
    volume calculation using UAV (Unmanned Aerial Vehicle)-based optical
    imagery and Light Detection And Ranging (LiDAR). The experiment was
    conducted at an apartment construction site in Daejeon, utilizing a DJI
    Matrice 350 RTK platform equipped with a Zenmuse L2 hybrid sensor. To
    ensure high-precision geometric alignment, a rigorous spatial framework
    integrating Ground Control Points (GCP) and VRS-RTK survey data was
    established.
    The analysis revealed that the terrain models derived from both sensors
    showed a relatively minor discrepancy in overall volume estimations.
    However, detailed sub-zone analysis indicated significant numerical
    variations depending on localized environmental factors. In cutting areas
    with deep shadows behind vertical retaining structures, digital
    photogrammetry tended to underestimate earthwork volumes due to
    surface-smoothing effects resulting from a loss of pixel contrast.
    Conversely, in fill and bare soil areas, photogrammetry tended to
    overestimate volumes due to high-frequency noise on low-texture
    surfaces and the filtering leakage of temporary onsite facilities.
    This study confirms that these localized underestimations and
    overestimations mathematically offset each other, resulting in a
    macro-level convergence of total volume estimates. These findings
    highlight the importance of itemized spatial analysis when comparing
    passive and active UAV sensors, providing a practical baseline for
    integrating smart construction technologies into earthwork management.
    번역하기

    This study evaluates the reliability and practical efficiency of earthwork volume calculation using UAV (Unmanned Aerial Vehicle)-based optical imagery and Light Detection And Ranging (LiDAR). The experiment was conducted at an apartment const...

    This study evaluates the reliability and practical efficiency of earthwork
    volume calculation using UAV (Unmanned Aerial Vehicle)-based optical
    imagery and Light Detection And Ranging (LiDAR). The experiment was
    conducted at an apartment construction site in Daejeon, utilizing a DJI
    Matrice 350 RTK platform equipped with a Zenmuse L2 hybrid sensor. To
    ensure high-precision geometric alignment, a rigorous spatial framework
    integrating Ground Control Points (GCP) and VRS-RTK survey data was
    established.
    The analysis revealed that the terrain models derived from both sensors
    showed a relatively minor discrepancy in overall volume estimations.
    However, detailed sub-zone analysis indicated significant numerical
    variations depending on localized environmental factors. In cutting areas
    with deep shadows behind vertical retaining structures, digital
    photogrammetry tended to underestimate earthwork volumes due to
    surface-smoothing effects resulting from a loss of pixel contrast.
    Conversely, in fill and bare soil areas, photogrammetry tended to
    overestimate volumes due to high-frequency noise on low-texture
    surfaces and the filtering leakage of temporary onsite facilities.
    This study confirms that these localized underestimations and
    overestimations mathematically offset each other, resulting in a
    macro-level convergence of total volume estimates. These findings
    highlight the importance of itemized spatial analysis when comparing
    passive and active UAV sensors, providing a practical baseline for
    integrating smart construction technologies into earthwork management.

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    목차 (Table of Contents)

    • Ⅰ. 서 론 ····················································································································1
    • 1. 연구배경 및 목적 ·····························································································1
    • 2. 연구 동향 ···········································································································2
    • 3. 연구 범위 및 방법 ···························································································4
    • Ⅱ. 초경량 무인비행장치 토공량 측량 ·····························································6
    • Ⅰ. 서 론 ····················································································································1
    • 1. 연구배경 및 목적 ·····························································································1
    • 2. 연구 동향 ···········································································································2
    • 3. 연구 범위 및 방법 ···························································································4
    • Ⅱ. 초경량 무인비행장치 토공량 측량 ·····························································6
    • 1. 초경량 무인 비행장치 ····················································································6
    • 1) 플랫폼 구성 ·····································································································6
    • 2. 광학영상 기반 3차원 공간정보 취득 ·························································8
    • 1) 기하모형 ···········································································································8
    • 2) 영상의 공간해상도 ····················································································10
    • 3) 수치 영상처리 알고리즘 ·········································································11
    • 4) 점군자료 생성 ······························································································12
    • 3. 라이다 기반 점군자료 생성 ········································································13
    • 1) 거리 및 각 관측 원리 ················································································13
    • 2) 점군자료 생성 ······························································································14
    • 3) 점군자료 기하보정 ······················································································15
    • 4. 수치지형모형 생성 ························································································16
    • 1) 수치지형모형 ································································································16
    • 2) TIN 기반 생성 ····························································································17
    • 3) 격자 기반 생성 ····························································································18
    • 5. 토공량 계산 ·····································································································18
    • 1) 격자 기반 계산 ····························································································19
    • 2) 불규칙 삼각망 기반 체적 계산 ·······························································19
    • Ⅲ. 실험 및 고찰 ···································································································21
    • 1. 연구 대상지 및 자료 ····················································································21
    • 1) 연구 대상지 ··································································································21
    • 2) 입력자료 구성 및 역할 ··············································································24
    • 3) 관측장비 ·······································································································29
    • 2. 수치지형모형 구축 ························································································32
    • 1) 설계도 및 지상측량 ····················································································32
    • 2) LiDAR 자료 ·································································································35
    • 3) 광학영상 자료 ······························································································38
    • 3. 구역 분할 및 토공량 산정 ··········································································42
    • 1) 구역 분할 ·······································································································42
    • 2) 토공량 산정 ··································································································46
    • 3) 센서별 토공량 차이 비교 기준 ·······························································48
    • 4. 토공량 산정 결과 비교 ················································································49
    • 1) 전체 산정 결과 분석 ··················································································49
    • 2) 구역별 분석 ··································································································50
    • 3) 토공량 차이가 큰 주요 구역의 원인 고찰 ·········································· 55
    • Ⅳ. 요약 및 결론 ···································································································58
    • 참고문헌 ··················································································································60
    • Abstract ··················································································································61
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