A low-cost unmanned aerial platform (UAV) equipped with RGB (Red, Green, Blue) and thermographic sensors is used for the acquisition of all the data needed for the automatic detection and evaluation of thermal pathologies on photovoltaic (PV) surfaces and geometric defects in the mounting on photovoltaic power stations. RGB imagery is used for the generation of a georeferenced 3D point cloud through digital image preprocessing, photogrammetric and computer vision algorithms. The point cloud is complemented with temperature values measured by the thermographic sensor and with intensity values derived from the RGB data in order to obtain a multidimensional product (5D: 3D geometry plus temperature and intensity on the visible spectrum). A segmentation workflow based on the proper integration of several state-of-the-art geomatic and mathematic techniques is applied to the 5D product for the detection and sizing of thermal pathologies and geometric defects in the mounting in the PV panels. It consists of a three-step segmentation procedure, involving first the geometric information, then the radiometric (RGB) information, and last the thermal data. No configuration of parameters is required. Thus, the methodology presented contributes to the automation of the inspection of PV farms, through the maximization of the exploitation of the data acquired in the different spectra (visible and thermal infrared bands). Results of the proposed workflow were compared with a ground truth generated according to currently established protocols and complemented with a topographic survey. The proposed methodology was able to detect all pathologies established by the ground truth without adding any false positives. Discrepancies in the measurement of damaged surfaces regarding established ground truth, which can reach the 5% of total panel surface for the visual inspection by an expert operator, decrease with the proposed methodology under the 2%. The geometric evaluation of the facilities presents discrepancies regarding the ground truth lower than one degree for angular parameters (azimuth and tilt) and lower than 0.05 m² for the area of each solar panel.

A low-cost multi-sensor aerial platform, aerial trike, equipped with visible and thermographic sensors is used for the acquisition of all the data needed for the automatic analysis and classification of roof surfaces regarding their suitability to harbor solar panels. The geometry of a georeferenced 3D point cloud generated from visible images using photogrammetric and computer vision algorithms, and the temperatures measured on thermographic images are decisive to evaluate the areas, tilts, orientations and the existence of obstacles to locate the optimal zones inside each roof surface for the installation of solar panels. This information is complemented with the estimation of the solar irradiation received by each surface. This way, large areas may be efficiently analyzed obtaining as final result the optimal locations for the placement of solar panels as well as the information necessary (location, orientation, tilt, area and solar irradiation) to estimate the productivity of a solar panel from its technical characteristics.

A mobile indoor mapping system combined with infrared thermography was used for the acquisition of data needed for the quantification of heat loss through a building envelope by conduction; that is, temperature values and building geometry. The methodology presented orthothermograms to provide measurement of heat loss and thermographic images with geometric information. This way, not only the energy loss through the building envelope is provided, but also thermographic information regarding the existence of thermal pathologies, their location and their impact on the building can also be evaluated.

Close-range photogrammetry and thermographic imaging techniques are used for the acquisition of all the data needed for the non-invasive assessment of a honeybee hive population. Temperature values complemented with precise 3D geometry generated using novel close-range photogrammetric and computer vision algorithms are used for the computation of the inner beehive temperature at each point of its surface. The methodology was validated through its application to three reference beehives with different population levels. The temperatures reached by the exterior surfaces of the hives showed a direct correlation with the population level. In addition, the knowledge of the 3D reality of the hives and the position of each temperature value allowed the positioning of the bee colonies without the need to open the hives. This way, the state of honeybee hives regarding the growth of population can be estimated without disturbing its natural development.