港區三維空間資訊同時包含海床、水下構造物、岸際立面及陸域設施，惟不同空間範圍須由不同量測平台取得，使水陸交界處容易產生資料缺口及幾何不一致。本研究以臺南安平亞果遊艇碼頭為研究區，整合無人船(Unmanned Surface Vehicle, USV)搭載之多音束測深儀(Multibeam Echo Sounder, MBES)與光達(Light Detection and Ranging, LiDAR)，以及無人機(Unmanned Aerial Vehicle, UAV)攝影測量成果，建立多平台點雲建置、品質驗證及比較分析流程。MBES成果透過國際海道測量組織(International Hydrographic Organization, IHO) S-44規範進行評估；USV LiDAR與UAV點雲則利用現地檢核點進行外部精度檢核。對於UAV與USV LiDAR重疊觀測之固定構造物表面，本研究採用多尺度模型對模型點雲比較法(Multiscale Model-to-Model Cloud Comparison, M3C2)分析其幾何一致性；對於缺乏共同表面之水上與水下點雲，則利用網格萃取水陸立面之有效量測邊界，計算垂直資料缺口。成果顯示，MBES成果符合IHO S-44特等要求垂直容許誤差門檻之比例為97.54%，總傳播不確定度網格之符合比例則為99.18%。USV LiDAR之平面均方根誤差(Horizontal Root Mean Square Error, HRMSE)與高程均方根誤差(Vertical Root Mean Square Error, VRMSE)分別為0.057 m及0.053 m，UAV點雲則分別為0.123 m及0.144 m。UAV與USV LiDAR全區平均絕對M3C2距離為0.266 m，而北堤護岸代表樣區固定立面之平均絕對M3C2距離降至0.052 m，顯示兩者之幾何差異受到構造物形態、遮蔽及觀測範圍影響。水陸交界分析顯示，UAV與MBES及USV LiDAR與MBES之平均垂直資料缺口皆約為1.40 m；加入USV LiDAR後，有效比較網格由322個增加至507個，增加57.5%，顯示USV LiDAR主要增加護岸立面與近水面區域可供分析之空間位置，而非縮小整體平均垂直資料缺口。由於各平台資料取得期次不同，上述缺口反映本研究最終點雲成果之空間分離情形，而非同時期之實際水陸間距。本研究建立之流程可用於評估多平台成果品質、共同表面幾何差異及水陸交界資料覆蓋關係，並作為港區多源三維資料建置與後續整合研究之參考。

Three-dimensional harbor information includes the seafloor, underwater structures, shoreline facades, and terrestrial facilities. Because these areas require different surveying platforms, data gaps and geometric inconsistencies may occur near the land–water interface. This study integrates Multibeam Echo Sounder (MBES), Light Detection and Ranging (LiDAR) with Unmanned Surface Vehicle (USV), and Unmanned Aerial Vehicle (UAV) photogrammetry at Anping Argo Yacht Marina, Tainan, to establish a workflow for multi-platform point-cloud construction, quality assessment, and comparison. MBES results were evaluated according to the International Hydrographic Organization (IHO) S-44 standard, while USV LiDAR and UAV point clouds were assessed using surveyed check features. Multiscale Model-to-Model Cloud Comparison (M3C2) was used to evaluate geometric differences on commonly observed fixed surfaces, and a grid-based method was applied to analyze vertical data gaps where continuous common surfaces were unavailable. The results show that 97.54% of the MBES observations satisfied the IHO S-44 Special Order vertical tolerance, while 99.18% of the Total Propagated Uncertainty (TPU) grid cells met the corresponding requirement. The Horizontal Root Mean Square Error (HRMSE) and Vertical Root Mean Square Error (VRMSE) were 0.057 m and 0.053 m for USV LiDAR, and 0.123 m and 0.144 m for UAV, respectively. The mean absolute M3C2 distance between UAV and USV LiDAR was 0.266 m over the full area and 0.052 m on the representative northern revetment surface. The mean vertical data gaps for both UAV–MBES and USV LiDAR–MBES were approximately 1.40 m. Adding USV LiDAR increased the number of valid comparison grid cells from 322 to 507, or 57.5%, mainly by increasing coverage of revetment facades and near-water-surface areas rather than reducing the mean vertical gap. Because the datasets were acquired at different times, these gaps represent the spatial separation of the final point clouds rather than contemporaneous land–water separation. The proposed workflow provides a basis for evaluating multi-platform data quality, geometric differences, and land–water spatial coverage in harbor environments.

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