<p>Automated detection and geolocation of roadside objects are critical for effective roadway safety analysis and transportation planning, particularly in rural areas. This paper presents an approach for detecting and geolocating stationary roadside objects by fusing airborne LiDAR data with videolog images. While multi-modal sensor fusion has been widely studied and applied in autonomous navigation for enhanced spatial perception, to the best of our knowledge, existing methods all assume known sensor parameters and dense spatiotemporal resolution to facilitate spatiotemporal data alignment. However, in practice, datasets may have incomplete sensor metadata and sparse spatiotemporal resolution. We aim to enable automated detection and geolocation of roadside objects using videolog data comprising over 43 million images of North Carolina’s rural roads. The videolog lacks camera intrinsic and pose parameters, and due to temporal downsampling of the initial video capture, consecutive images are spaced 26 feet apart, and GPS coordinates must be approximated. To address these limitations, we have integrated airborne LiDAR data with videolog images through a novel data registration and alignment approach, which estimates missing camera parameters through minimization of alignment errors between videolog road lane markings and projected LiDAR road edges, enabling more accurate computation of object bearings in our geolocation pipeline. Using utility poles as a case study, we demonstrate the effectiveness of our pipeline in detecting and geolocating roadside objects. This work contributes a practical and scalable solution to the often-overlooked challenge of sensor fusion with incomplete camera metadata.</p>