Sustainability 2021, 13, 5323. https://doi.org/10.3390/su13095323 www.mdpi.com/journal/sustainability Article Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks Konstantinos Ioannou 1, * and Dimitrios Myronidis 2 1 Hellenic Agricultural Organization ”DEMETER”, Forest Research Institute, 57006 Thessaloniki, Greece 2 Department of Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; myronid@for.auth.gr * Correspondence: ioanko@fri.gr; Tel.: +30‐231‐046‐1171 (ext. 225) Abstract: The number of solar photovoltaic (PV) arrays in Greece has increased rapidly during the recent years. As a result, there is an increasing need for high quality updated information regarding the status of PV farms. This information includes the number of PV farms, power capacity and the energy generated. However, access to this data is obsolete, mainly due to the fact that there is a difficulty tracking PV investment status (from licensing to investment completion and energy production). This article presents a novel approach, which uses free access high resolution satellite imagery and a deep learning algorithm (a convolutional neural network—CNN) for the automatic detection of PV farms. Furthermore, in an effort to create an algorithm capable of generalizing better, all the current locations with installed PV farms (data provided from the Greek Energy Regulator Authority) in the Greek Territory (131,957 km 2 ) were used. According to our knowledge this is the first time such an algorithm is used in order to determine the existence of PV farms and the results showed satisfying accuracy. Keywords: PV farms; deep learning; satellite imagery; CNN; automatic detection 1. Introduction During the last three decades mankind is witnessing an evolution in the energy sector as we notice a shift in energy production methods, from the usage of fossil fuels (petroleum, natural gas, coal, etc.) to more environmentally friendly methods. This is caused mainly due to the fact that a significant portion of the worldʹs carbon dioxide production is a result of fossil fuels used for energy production [1–3]. However, as electricity consumption plays an important role for modern societies (and its usage cannot be reduced) other forms of energy production must be used in order to satisfy current and future energy demands [3–7]. Renewable energy methods can be considered as a viable solution for energy production and the reduction of CO2 emissions. These methods include the usage of sustainable sources based on wind, water, biomass, solar and geothermal energy for energy production which are in general called renewable energy sources (RES) [8]. The exploitation of solar energy is considered as one of the most common types of RES. Solar panels are used for transforming energy from indecent sunlight, to electricity using solar cells based on the photovoltaic effect, thus they are also called photovoltaic (PV) panels [9]. Nowadays, massive arrays of PV panels (in the form of solar or PV farms) are used for energy production throughout the world. These farms energy production capability ranges from 1 to 2000 MW, in the case of mega projects covering thousands of hectares [10]. In Europe, PV farms account for 13% of the total RES production. Furthermore, solar power is the fastest‐growing source: in 2008, it accounted for 1%. This means that the Citation: Ioannou, K.; Myronidis, D. Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks. Sustainability 2021, 13, 5323. https://doi.org/10.3390/su13095323 Academic Editor: Maria Malvoni Received: 15 April 2021 Accepted: 7 May 2021 Published: 10 May 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses /by/4.0/).