Ground Control Point (GCP) rectification serves an important role in assuring the absolute and relative accuracy for drone photogrammetry generated data, yet the process of identifying and marking GCPs is still handled manually, hindering the scalability of the source photo processing pipeline. In this paper, we propose a method to accurately detect and automatically mark GCPs from aerial images using deep learning and photogrammetry-generated sparse point cloud to expedite the sourcephoto processing pipeline. Using SOTA Object Detection and Image Classification models, RetinaNet and Inception-ResNet-V2, we first accurately detect Ground Control Points from the collected source photos. The detected targets are then filtered and labeled by backward-projecting the detected image x-y coordinates to 3D sparse point cloud, and comparing the 3D coordinate with the surveyed GCP. The GPS matching process on the sparse point cloud assures sub-centimeter level accuracy errors compared to traditional human rectification while exceeding the performance of other commercially available GCP detection methods.