空載光達為我國建立數值高程模型（Digital Elevation Model, DEM）之資料來源，然既有點雲分類演算法能力有限，使各廠商需投入大量人力編修點雲分類成果，以維持DEM品質。為加速地面點分類，本研究建立了一套基於幾何特徵的空載光達地面點人工智慧（Artificial Intelligence, AI）分類模式，光達點雲之幾何特徵資訊經投影至影像網格，以建立特徵影像，訓練UNet架構之神經網路。最後透過反投影機制，回饋影像分類成果至點雲，達成點雲分類。以城市區、農田區、森林區三個測試圖幅為例，使用AI分類之地面點產生之DEM與測繪廠商經檢核後之DEM，二者之高程差，分別有85.5%、94.6%、74.3%圖幅面積在空載光達觀測精度範圍±20cm內。本研究亦建議AI模型輸出之信心值，依地表環境設定不同地面點分類門檻值，提升人機協作效率。

Airborne Laser Scanning (ALS) can efficiently acquire large-scale point cloud data with high accuracy, which has become the major data source for Taiwan Digital Elevation Model (DEM). When generating ALS DEM, a significant amount of manual editing is needed to ensure the ground point classification, which are later used for DEM interpolation. In order to alleviate the manual burden, this research proposed an artificial intelligence (AI) ground classification workflow based on the geometric features from the ALS data. The geometric features are calculated and orthogonally projected to compose a "feature image", which was further used as the training data for UNet. Then, by back-projecting the image classification results, the ground point within the ALS data can be classified. Three example datasets, including city, county, and forest scenes, were examined. The results showed that, in terms of areal percentage, 85.5%, 94.6%, and 74.3% of the AI-derived DEM are within ± 20 cm of the QC-inspected DEM for city, county, and forest scene, respectively. We further suggested that the confidence value output from the AI classifier can be used as an adaptive parameter to facilitate manually point cloud editing. Different threshold can be devised for different scene.