At Mapsens® Solar we work on optimizing the processing of thermal data and its subsequent analysis for the supervision of solar power plants. In this example, we show you some data from a solar power plant obtained with a eBee Plus with thermoMap thermal camera and S.O.D.A. RGB camera. In the example we show you an interactive viewer where you can see the different layers of information obtained as well as the warnings automatically detected with the problems in the solar panels.

Aware of the high level of dedication required for the maintenance of photovoltaic power plants, at Mapsens® Solar we are working to minimize your time and maximize the use of drones in this field. Combining different methodologies, our application and our data collection generates a continuous mosaic of the solar plant, which allows us to quickly analyze anomalous thermal behavior in the panels (failures, malfunctions, etc.) in an automatic and geolocalized way. In this way, the application not only locates the anomalous points in an interactive viewer, but also incorporates task assignment systems, error databases, and the generation of automatic inspection reports.

In the following viewer we show you in an interactive way a real example of a photovoltaic plant that we are working with Mapsens® Solar. The drone used for the flights is a eBee Plus with thermoMap thermal camera and S.O.D.A. RGB camera. The truth is that this equipment gives excellent results for these jobs.

In the dropdown you can select the following layers of information:

  • Orthoimage: Based on this data, a digitalization and inventory of the panels is carried out according to the customer’s coding..
  • Thermal: By processing the thermal images we generate a detailed map of the thermal signature of the surface of each panel in a geolocalized way.
  • Map of alerts: From an automatic analysis, panels with anomalous behavior and which are susceptible to be reviewed are generated in a geolocalized way.


At the moment, thanks to the experience gained from our customers, we are working on the differentiation of the type of warning so that the application not only indicates if there is an anomalous behavior but also differentiates the possible causes. We continue to make progress.


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