Pattern recognition of photovoltaic panels

How do we detect solar panel locations using aerial imagery?

We use deep learning methods for automated detection of solar panel locations and their surface area using aerial imagery. The framework, which consists of a two-branch model using an image classifier in tandem with a semantic segmentation model, is trained on our created dataset of satellite images.

Why are new data-driven models needed for photovoltaic (PV) energy measurements?

With the rapid growth in computational complexities of statistical pattern recognition of photovoltaic (PV) energy measurements, the need for new data-driven models has emerged.

Can remote sensing and machine learning improve photovoltaic data collection?

In the United States, for example, the most comprehensive database of photovoltaic installations 3 covers only around 80% of installations. Collection of these data is expensive and often impeded by regulatory or institutional barriers — which is why approaches based on remote sensing and machine learning offer a practical alternative.

Can thermal imaging detect faults in PV panels?

Since the faults mainly appear as Hot Spots on the surface of the PV panels, aerial thermal imaging can be used to diagnose such problems and also locate them in huge plants. To this aim, dedicated automatic Computer Vision methods are able to automatically find hot spots from thermal images, where they appear as white stains.

Can a flying system detect faults in photovoltaic plants?

An intelligent flying system for automatic detection of faults in photovoltaic plants. Journal of Ambient Intelligence and Humanized Computing, pages 1--14, 2019. Mohammadreza Aghaei, Sonia Leva, and Francesco Grimaccia. Pv power plant inspection by image mosaicing techniques for ir real-time images.

Can machine learning identify photovoltaic installations in high-resolution aerial and satellite imagery?

Machine-learning approaches for identifying photovoltaic installations in high-resolution aerial and satellite imagery have grown at an impressive speed. The method was first proposed 4 – 6 in 2016 — for example, as a way of finding residential installations in an area of 135 square kilometres across Fresno, California 4.