Automated assessment of work-in-progress using large collections of site images and 4D BIM has potential to significantly improve the efficiency of construction project controls. Nevertheless, today's manual procedures for taking site photos do not support the desired frequency or completeness for automated progress monitoring. While the usage of Unmanned Aerial Vehicles for acquisition of site images has gained popularity, their application for addressing issues associated with image-based progress monitoring and particularly leveraging 4D BIM for steering the data collection process has not been investigated before. By presenting examples from two case studies conducted on real-world construction projects, this paper suggests a framework for model-driven acquisition and analytics of progress images. In particular, the potential of spatial (geometry, appearance, and interconnectivity) and temporal information in 4D BIM for autonomous data acquisition and analytics that guarantees completeness and accuracy for both as-built modeling and monitoring work-in-progress at the schedule task-level is discussed.