Unmanned Aerial Vehicle (UAV) Photogrammetry has become an important tool for high-resolution mapping, with workflows increasingly incorporating onboard GNSS solutions such as Post-Processed Kinematic (PPK) to reduce dependence on Ground Control Points (GCPs). However, the performance of PPK in complex terrain remains uncertain. This study evaluates the positional accuracy of UAV-derived orthomosaics generated using GCP-based and PPK-based workflows over the hilly terrain of the Umiam Reservoir, Meghalaya, India, and applies machine learning to analyze terrain-induced error patterns. Accuracy assessment against DGPS-surveyed ground truth showed that the GCP-based workflow achieved sub-decimeter accuracy (RMSEX = 0.043 m, RMSEY = 0.031 m, RMSEZ = 0.072 m; CE90 = 0.080 m; LE90 = 0.119 m). In contrast, the PPK-based workflow displayed larger deviations (RMSEX = 1.025 m, RMSEY = 1.240 m, RMSEZ = 2.980 m; CE90 = 2.441 m; LE90 = 4.902 m), with particularly high vertical errors. Machine Learning (ML) provided additional insights: regression analysis revealed that slope explained ~60% of the variance in vertical error (R2 = 0.605), while clustering identified three error regimes, i.e., systematic vertical underestimation, systematic vertical overestimation, and horizontal drift. The results highlight a trade-off between field efficiency and accuracy. While PPK reduces GCP requirements, its vertical reliability declines sharply in rugged terrain. The novelty of this work lies in using ML-assisted diagnostics to characterize terrain-induced error regimes, offering insights beyond conventional RMSE-based evaluations. This study concludes that hybrid approaches, combining PPK with sparse GCPs or terrain-aware ML corrections, can balance efficiency and reliability. The findings have practical significance for UAV mapping in the hilly landscapes of Northeast India and other complex terrains worldwide.
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