Over the last decade, there has been an important growing trend towards the use of technology to perform building inspections within the Architecture, Engineering and Construction (AEC) industry. In recent years, researchers have shown an increased interest in Non-Destructive Testing (NDT) based on scanning and surveying diagnostics such as Ground Penetrating Radar (GPR), Light Detection and Ranging (LiDAR), Laser Scanning, and Close-Range Photogrammetry (CRP). To date however, not enough research has been developed to explore durability approach diagnostic inspection. This paper describes the design and implementation of an approach using Close-Range RGB Photogrammetry based on Remote Piloted Aircraft System (RPAS) surveying for durability failure detection and inspection in heritage buildings. The RPAS utilizes CRP approach to capture a series of images and then develop an RGB processed orthophoto. The detection approach uses deep learning to classify durability failures in the structure using MATLAB. Finally, the proposed approach was tested in a bridge in Guanajuato, Mexico. Results demonstrated a highly practical and low technological resources approach for durability diagnosis inspection to obtain the damage survey of the evaluated bridge.