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However, the issue of scale error continues to hinder its practical applicability. This paper seeks to address this limitation by deploying advanced georegistration modalities integrated into the structure\u2010from\u2010motion (SfM) pipeline, leveraging either conventional ground control points (GCPs) or ArUco markers. Traditionally, georegistration modules rely heavily on the precise positioning of GCPs, which imposes significant challenges due to the inefficiency and limited precision of manual measurements. To enhance usability and streamline workflows, this research introduces an optional automated framework encompassing key processes, including image acquisition, the incorporation of marker\u2010derived 3D positions, and georegistration, ultimately producing accurate and lightweight 3D maps. Experiments on the Semantic Drone Dataset and a real\u2010world unmanned aerial vehicle (UAV) dataset show that GCP\u2013based registration achieves positional errors of 1\u20135\u2009m, while ArUco\u2010based reconstruction attains mapping errors below 6% with marker pose estimation errors under 8%. In addition, the octree representation reduces memory usage by 83.5% on public data and by nearly 100x in real\u2010world scenarios. 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