Correction: García-Balboa, J.L., et al. A Field Procedure for the Assessment of the Centring Uncertainty of Geodetic and Surveying Instruments. Sensors 2018, 18, 3187.

The authors wish to make the following corrections to this paper [...

Variables Influencing the Accuracy of 3D Modeling of Existing Roads Using Consumer Cameras in Aerial Photogrammetry.

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.

A Field Procedure for the Assessment of the Centring Uncertainty of Geodetic and Surveying Instruments.

The uncertainty evaluation of survey measurements is a daily and essential task in any surveying work. The result of a measurement is, in fact, only complete when accompanied by a statement of its uncertainty. Miscentring, or centring error, is one of the sources of uncertainty in every basic survey measurement which may have a great effect on horizontal angle measurement for short distances.