Accurate 3D reconstruction of construction sites is essential for progress tracking, safety management, and digital twin applications. However, UAV-based photogrammetry is limited in capturing dynamic scene changes, while surveillance cameras lack depth sensing and accurate calibration. To overcome these limitations, we propose a hierarchical reconstruction framework that integrates UAV imagery with surveillance image for scalable, real-time 3D reconstruction. Specifically, we first generate a metrically accurate 3D map from UAV images using GPS/RTK data and ground control points. Next, virtual views are rendered from this map and matched with surveillance images to localize surveillance cameras without manual calibration. Furthermore, by aligning monocular depth maps of surveillance images with the rendered depths, we calibrate the depth predictions to metric scale, enabling near real-time reconstruction of dynamic scene changes. In addition, our method demonstrates robust localization and reconstruction performance under challenging conditions such as weather variations and structural changes, making it well-suited for long-term deployment in dynamic construction environments.