Photorealistic three-dimensional (3D) models play an indispensable role in the spatial data infrastructure (SDI) of a smart city. Recent developments in aerial oblique photogrammetry, and the popularity of terrestrial mobile mapping systems (MMSs) offer possibilities for deriving 3D models with centimeter-level accuracy in urban areas. Additionally, advances in image matching and bundle adjustment have allowed 3D models derived from the integration of aerial and ground imagery to overcome typical problems related to 3D mapping in urban areas (e.g., geometric defects, blurred textures on building façades). Nevertheless, this approach may not be suitable for all scenarios owing to innate differences between each platform. Besides, MMS images may not cover regions that cannot be reached by mobile vehicles in urban areas (e.g., narrow alleys, areas far from roads). Meanwhile, backpack systems have garnered attention from the photogrammetry community in recent years due to their flexibility, and regions neglected in previous works can be adequately reconstructed from images collected by backpack systems. This paper presents an approach for effectively integrating multi-source images collected by aerial, MMS, and backpack platforms for seamless 3D mapping in urban areas. The approach includes three main steps: (1) data pre-processing, (2) combined structure-from-motion, and (3) optimal generation of a textured 3D mesh model. The experimental results using aerial, MMS, and backpack datasets collected in a typical urban area in Hong Kong demonstrate the promising performance of the proposed approach. The described work is significant for boosting various types of imagery for integrated 3D mapping in both city scale and street level to facilitate various applications.