Construction is an area of study wherein making decisions adequately can mean the difference between success and failure. Moreover, most of the activities belonging to this sector involve taking into account a large number of conflicting aspects, which hinders their management as a whole. Multi-criteria decision making analysis arose to model complex problems like these. This paper reviews the application of 22 different methods belonging to this discipline in various areas of the construction industry clustered in 11 categories. The most significant methods are briefly discussed, pointing out their principal strengths and limitations. Furthermore, the data gathered while performing the paper are statistically analysed to identify different trends concerning the use of these techniques. The review shows their usefulness in characterizing very different decision making environments, highlighting the reliability acquired by the most pragmatic and widespread methods and the emergent tendency to use some of them in combination

Rock art is one of the most fragile and relevant cultural phenomena in world history, carried out in shelters or the walls and ceilings of caves with mineral and organic substances. The fact it has been preserved until now can be considered as fortunate since both anthropogenic and natural factors can cause its disappearance or deterioration. This is the reason why rock art needs special conservation and protection measures. The emergence of digital technologies has made a wide range of tools and programs available to the community for a more comprehensive documentation of rock art in both 2D and 3D. This paper shows a workflow that makes use of visible and near-infrared hyperspectral technology to manage, monitor and preserve this appreciated cultural heritage. Hyperspectral imaging is proven to be an efficient tool for the recognition of figures, coloring matter, and state of conservation of such valuable art.

Rock art offers traces of our most remote past and was made with mineral and organic substances in shelters, walls, or the ceilings of caves. As it is notably fragile, it is fortunate that some instances remain intact—but a variety of natural and anthropogenic factors can lead to its disappearance. Therefore, as a valuable cultural heritage, rock art requires special conservation and protection measures. Geomatic remote-sensing technologies such as 3D terrestrial laser scanning (3DTLS), drone flight, and ground-penetrating radar (GPR) allow us to generate exhaustive documentation of caves and their environment in 2D, 2.5D, and 3D. However, only its combined use with 3D geographic information systems (GIS) lets us generate new cave maps with details such as overlying layer thickness, sinkholes, fractures, joints, and detachments that also more precisely reveal interior–exterior interconnections and gaseous exchange; i.e., the state of senescence of the karst that houses the cave. Information of this kind is of great value for the research, management, conservation, monitoring, and dissemination of cave art.

There is a growing demand for measurements of natural and built elements, which require quantifiable accuracy and reliability, within various fields of application. Measurements from 3D Terrestrial Laser Scanner come in a point cloud, and different types of surfaces such as spheres or planes can be modelled. Due to the occlusions and/or limited field of view, it is seldom possible to survey a complete feature from one location, and information has to be acquired from multiple points of view and later co-registered and geo-referenced to obtain a consistent coordinate system. The aim of this paper is not to match point clouds, but to show a methodology to adjust, following the traditional topo-geodetic methods, 3DTLS data by modelling references such as calibrated spheres and checker-boards to generate a 3D trilateration network from them to derive accuracy and reliability measurements and post-adjustment statistical analysis. The method tries to find the function that best fits the measured data, taking into account not only that the measurements made in the field are not perfect, but that each one of them has a different deviation depending on the adjustment of each reference, so they have to be weighted accordingly.

Accurate determination of water depth is indispensable in multiple aspects of civil engineering (dock construction, dikes, submarines outfalls, trench control, etc.). To determine the type of atmospheric correction most appropriate for the depth estimation, different accuracies are required. Accuracy in bathymetric information is highly dependent on the atmospheric correction made to the imagery. The reduction of effects such as glint and cross-track illumination in homogeneous shallow-water areas improves the results of the depth estimations. The aim of this work is to assess the best atmospheric correction method for the estimation of depth in shallow waters, considering that reflectance values cannot be greater than 1.5 % because otherwise the background would not be seen. This paper addresses the use of hyperspectral imagery to quantitative bathymetric mapping and explores one of the most common problems when attempting to extract depth information in conditions of variable water types and bottom reflectances. The current work assesses the accuracy of some classical bathymetric algorithms (Polcyn–Lyzenga, Philpot, Benny–Dawson, Hamilton, principal component analysis) when four different atmospheric correction methods are applied and water depth is derived. No atmospheric correction is valid for all type of coastal waters, but in heterogeneous shallow water the model of atmospheric correction 6S offers good results.