Abstract: The "cloud control" paradigm, grounded on geocoded data such as digital orthophoto map (DOM) and digital elevation model (DEM), is gradually maturing for applications in medium and small-scale mapping. However, its essence of obtaining control points from the object space inherently carries errors within the DOM and DEM products, subsequently hindering its applicability in large-scale mapping scenarios. Constructing constraints based on original images with known accurate orientation parameters and adjusted object space tie points represents a theoretically superior cloud control paradigm. This paper proposes developing this image-based cloud control paradigm into a standardized survey and mapping product, termed digital control photo (DCP), aiming to better ensure positioning accuracy while reducing redundant labor and computational power consumption in revisit aerial survey tasks. This paper also introduces the application of DCP in calibration of large-format aerial cameras, geometric positioning of unmanned aerial vehicle images and satellite images, and suggests several recommendations for promoting the establishment of a DCP application system.