本研究提出一套基於無人機（UAV）攝影測量點雲之 LOD-2（第二級細節層度）三維屋頂模型重建及屋頂太陽能潛勢評估的整合式框架。所提出之框架整合了深度學習點雲分類、屋頂平面分割、三維幾何重建、太陽輻射模擬，以及多準則決策分析自動判斷適合設置太陽能板之屋頂位置。首先，利用 PointCNN 深度學習模型進行建築物點雲分類，整體分類精度達 93.27%。隨後，採用基於密度之聚類方法完成建築物實例分群，並透過屋頂平面分割識別各屋頂平面。為了重建屋頂及建築物輪廓幾何結構，本研究採用基於 Alpha Shape 演算法的幾何重建方法建立 LOD-2 屋頂模型。接著，以數位地表模型（DSM）估算屋頂太陽輻射量。最後，結合屋頂坡度、坡向、屋頂可利用面積及太陽輻射等因素，進行多準則評估，以篩選最適合安裝太陽能板之屋頂平面，並據此估算最佳太陽能板配置數量及其對應之太陽能發電潛力。實驗結果顯示，所提出之框架於建築物重建評估中，精確率（Precision）達 90.20%、召回率（Recall）達 93.56%、F1 分數達 91.84%，以及交並比（Intersection over Union, IOU）達 84.93%。此外，所重建之 LOD-2 屋頂模型具有良好的幾何一致性與可靠性。屋頂平面適宜性分析結果亦證明，本研究所提出之框架能夠有效建立屋頂平面尺度之太陽能潛勢分布圖，適用於大範圍都市地區之太陽能潛勢評估。整體而言，本研究提出之方法可提供一套高效率、穩健且自動化的 LOD-2 屋頂模型重建與屋頂太陽能潛勢評估流程，並可作為都市能源規劃、再生能源推廣及永續低碳城市發展之重要決策支援工具。

This study proposes an integrated framework for reconstructing LOD-2 (Level of Detail) 3D roof models and assessing roof solar potential based on Unmanned Aerial Vehicle (UAV) photogrammetric point clouds. The proposed framework integrates deep learning point cloud classification, roof plane segmentation, 3D geometric reconstruction, solar radiation simulation, and multi-criteria decision analysis to automatically determine suitable roof for solar panel installation. First, the PointCNN deep learning model is used for building point cloud classification, achieving an overall classification accuracy of 93.27%. Then, density-based clustering is used to group building instances, and roof plane segmentation identifies each roof plane. To reconstruct the roof and building outline geometry, an LOD-2 roof model is established using a geometric reconstruction method based on the Alpha Shape algorithm. Furthermore, a digital surface model (DSM) is used to estimate roof solar radiation. Finally, a multi-criteria evaluation is performed, considering factors such as roof slope, aspect, usable roof area, and solar radiation, to select the most suitable roof plane for solar panel installation and estimate the optimal number of solar panels and their corresponding potential solar power generation. Experimental results show that the proposed framework achieves a precision of 90.20%, a recall of 93.56%, an F1 score of 91.84%, and an intersection over union (IOU) ratio of 84.93% in building reconstruction assessment. Furthermore, the reconstructed LOD-2 roof model exhibits good geometric consistency and reliability. Roof plane suitability analysis also demonstrates that the proposed framework can effectively establish a solar potential distribution map at the roof plane-scale, making it suitable for solar potential assessment in large-scale urban areas. Overall, the method proposed in this study provides a highly efficient, robust, and automated process for LOD-2 roof model reconstruction and roof solar potential assessment, and can serve as an important decision support tool for urban energy planning, renewable energy promotion, and sustainable low-carbon city development.

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