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.

In this paper, we describe and discuss emergent digital technologies employed for monitoring the excavations at the archaeological site of Barranco León (Granada, Spain) between 2017 and 2020. The method entails the following material requirements: a total station, a conventional camera, a computer and a software that integrates photogrammetry algorithms; in terms of human resources, the presence of an engineer for the first day of the excavation to set up the methodological process and trained excavation staff to perform said method on a daily basis is necessary. Here we present the daily workflow for monitoring archaeological excavations introduced at the site, followed by an estimation of the costs and a summary of the results obtained. The method introduced here is based on the height difference of Digital Elevation Models (DEMs) computed on successive days using photogrammetric techniques. It is a non-invasive method, which requires less than 8% of the economic resources of the excavation and can be carried out in less than 15 minutes. This allows the excavation director to have an accurate and visual idea of the excavation process in order to make appropriate decisions. In addition, from the results obtained in the method (differences in DEM of every two days), other derived results can be obtained such as the exact location of the archaeological remains extracted based on their size. These features highlight the relevance and applicability of this approach to a wide range of archaeological sites.

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.

Most theories of detection of deception relate lying to three factors: (1) cognitive load, (2) subjective arousal, and (3) convincing impression. The evidence suggests that a higher temperature of the forehead is related to cognitive load, a lower temperature of the nose is associated with subjective arousal, and a higher temperature of the cheeks is related to convincing impression. Here, we took into account these three factors and, at the same time, associated the thermal change in specific facial regions of interest (ROIs) with each one of them. More importantly, we studied the combination of the thermal changes in the ROIs to establish the best combination to detect deception. Our results confirm an association between thermal changes in different ROIs and the three factors above. The best combination in the thermal changes of the ROIs for detecting deception (producing 83% accuracy and 13% false alarms in Experiment 1) is the one that was termed ‘at least two of the three ROIs’ where there is a lower temperature of the nose and/or a higher temperature of the cheeks and/or a higher forehead temperature. This finding constitutes an advance for detecting deception in multiple forensic contexts.

There is still no reliable solution for obtaining Digital Terrain Models (DTM) under the canopy in areas with difficult access on foot. The difficulty lies in the configuration of the environment. This configuration does not allow access on foot to all areas of the terrain. In addition, the dense leaf litter complicates any photogrammetry technique. Currently, LiDAR is a technique that could help solve this problem. The best solution to this problem requires many samples over hostile environment and LiDAR flight simulation can be a good option to approximate the problem. For this reason, this research carries out a series of LiDAR flight simulations, using Helios++, in different types of scenes under the canopy with the aim of analysing the influence of the density of trees and the mean terrain slope over the number of points that bounce off the terrain. Additionally, this research enables the scientific community to approach the problem and guide future research based on the obtained results.