This paper presents a novel methodological framework for georeferenced traffic monitoring that bridges the gap between image-based vehicle detection and geospatial analysis. Traditional video-based traffic monitoring systems operate exclusively in image space, limiting their utility for applications requiring physical measurements and integration with other geospatial datasets. We address this limitation by developing a comprehensive camera calibration approach that leverages readily available geospatial data—including smartphone video ground control point selection, a hierarchical calibration algorithm for camera parameter estimation, and a coordinate transformation approach for mapping image-space vehicle detections to geographic space. Experimental results demonstrate the effectiveness of our strategy, achieving a mean reprojection error of 3 pixels across the calibration points. We showcase the practical utility of the framework through a case study of multi-lane traffic monitoring, where vehicle detections are successfully transformed from image coordinates to geographic coordinates, enabling lane-specific traffic analysis and potential integration with traffic simulation models. The methodology presented in this paper allows for precise mathematical relationships between image coordinates, drone-derived orthophotos, and 3D point cloud data, thereby establishing exact correspondences between image coordinates and real-world geographic coordinates. The proposed methodology includes a robust workflow for urban planning by connecting conventional video surveillance with the rich analytical capabilities of geographic information systems, using only commonly available data sources and equipment.