Certain historical works of civil engineering should be preserved as heritage monuments and when possible should continue serving the function they were designed for. Old stone bridges could be sustainably maintained but their conservation requires accurate documentation. In this study, we have scanned Ízbor bridge (1860) in Spain, and to facilitate conservation, we have modeled the ancient bridge using BIM (building information modeling). We propose a method and a model for this kind of bridge to be used as a reference for similar heritage monuments. Ízbor bridge modeled in this way will be useful for government planning and conservation agencies.

Point cloud (PC) generation from photogrammetry–remotely piloted aircraft systems (RPAS) at high spatial and temporal resolution and accuracy is of increasing importance for many applications. For several years, photogrammetry–RPAS has been used to recover civil engineering works such as digital elevation models (DEMs), triangle irregular networks (TINs), contour levels, orthophotographs, etc. This study analyzes the influence of variables involved in the accuracy of PC generation over asphalt shapes and determines the most influential variable based on the development of an artificial neural network (ANN) with patterns identified in the test flights. The input variables were those involved, and output was the three-dimension root mean square error (3D-RMSE) of the PC in each ground control point (GCP). The result of the study shows that the most influential variable over PC accuracy is the modulation transfer function 50 (MTF50). In addition, the study obtained an average 3D-RMSE of 1 cm. The results can be used by the scientific and civil engineering communities to consider MTF50 variables in obtaining images from RPAS cameras and to predict the accuracy of a PC over asphalt based on the ANN developed. Also, this ANN could be the beginning of a large database containing patterns from several cameras and lenses in the world market.