The integration of renewable energy sources, such as solar power harnessed through photovoltaic panels, within the context of a smart grid has contributed to diminished reliance on conventional fossil fuel-based power generation facilities [1,2]. Photovoltaic (PV) systems are one of the most promising low-carbon energy generation methods [3]. PV energy production has grown rapidly over the last decade, at a rate of more than 35% annually [4,5]. At the end of 2022, the world’s cumulative installed PV capacity was 1055.03 GW, compared to 589.43 GW in 2019, almost doubling in three years [6].

Estimating the total installed PV capacity and power generation can enhance the ability of policymakers and stakeholders to evaluate progress in terms of sustainability, quantify the actual benefits of green energy, and consider potential future installations [7]. Aerial and satellite images have been analysed to recognise PV panels by means of approaches using machine learning (ML), i.e., convolutional neural networks (CNN), deep learning methods [8,9,10,11,12,13,14,15,16,17], and random forests [18,19,20,21]. 

Further approaches have focused on analysing the physical absorption and reflection characteristic of PV panels to identify them from airborne images [22,23]. Another approach aims at identifying PV panels by means of a deterministic algorithm that carefully and extensively analyses the colours of the pixels forming the panels [33].

Estimating the number of PV panels in a region is a complex task due to the insufficiency (or even lack of) official registers. Many papers have proposed approaches to detect PV systems by analysing satellite and aerial images, often using Convolutional Neural Networks (CNN) or Random Forest (RF) classifiers.

A deep neural network model called Faster-RCNN was used to design the identification model of PV panels [12]. The approach consisted of two parts: first, a ResNet-50 classifier was pretrained, then a CNN was fine-tuned for the identification task of rooftop PV panels. Similarly, three convolutional layers and three fully connected layers were used to evaluate the performance of the identification [8]. Moreover, eight 2D convolutional layers were used to detect PV panels in residential areas; to achieve the best performance, thirteen architectures were trained and the most accurate was selected [9]. Other approaches have used InceptionV3, a CNN used for image analysis and object detection, which was fine-tuned for the task of PV panel identification [10,11]. These approaches were designed to detect PV panels in both residential and non-residential areas; however, due to the lack of PV panel images, data augmentation was performed during the training process. The framework proposed in [10] was employed for the detection of PV panels in Sweden to collect further market statistics [24]. Similarly, an innovative approach was presented to detect rooftop PV panels on the three-dimensional (3D) orientation [25]. This approach employed the InceptionV3 model to classify images; subsequently, segmentation and geocoding steps were performed to analyse the 3D images. ML and deep learning techniques were used for rooftops PV panels detection in [13]. The k-means approach was applied to segment the images in order to define the contours of each rooftop, then a support vector machine (SVM) classifier with a CNN was integrated to accurately identify solar PV arrays. A Mask-RCNN was used for segmentation and identification in [14,16,17]. These approaches applied the object detection technique to reveal PV panels on aerial images, with CNN being fine-tuned to characterise the mask contours used for the arrays. A CNN with the VGG16 encoder was presented in [15]; first, image segmentation was performed to select the suitable portions of solar panels, then the azimuth of the solar arrays was predicted using edge detection and the Hough transform.

A different approach was proposed in [18] to extract image features such as colours, textures, and other patterns from each pixel, then pass them as input to train an RF classifier to identify pixels related to PV arrays. In a similar approach [19], the focus was on the identification of water PV systems (WPV); an RF classifier with 400 trees was trained to extract pixels related to WPV, then postprocessing was performed to remove noise and rooftop PV panels. Another pixel-based RF algorithm used the L-8 surface reflectance (SR) product to identify suitable PV panels [20]. The RF classifier was based on the Google Earth Engine (GEE) and used to map PV power plants. Similarly, an RF classifier for an Object-Based Image Analysis (OBIA) approach used different combinations of multispectral Sentinel-2 imagery and radar backscatter from Sentinel-1 SAR imagery [26]. In [21], RF classification was combined with a CNN. First, the RF was used to assign a confidence value to each pixel in order to determine the possibility of that pixel belonging to a solar PV array; then, a CNN was used to classify 40×40 patches of RGB images to determine whether or not they corresponded to solar PV panels. An innovative deep learning technique called EfficientNet-B7 was employed for PV panel detection in [27], showing better accuracy and efficiency compared to classic CNN approaches. EfficientNet-B7 was used as a backbone encoder to train a U-Net model for segmenting solar panels.

Spectral characteristics have been investigated to detect PV panels from hyperspectral data [22,23] by focusing on the physical absorption and reflection characteristics of PV panels. To handle the material diversity of PV panel types, these studies applied a tailored image spectral library, which together with the hydrocarbon index mitigated the spectral variance caused by the detection angle. 

An approach for performing the detection by extracting and utilising characterising colours of PV panels has been presented in [33]. It defines a deterministic algorithm that carefully and extensively analyses the colours of the pixels forming the panels. The approach can detect photovoltaic panels conforming to a properly formed significant range of colours extracted according to the given conditions of light exposure in the analysed images. The significant range of colours is automatically formed from an annotated dataset of images, and consists of the most frequent panel colours differing from the colours of surrounding parts. Such colours are then used to detect panels in other images by analysing panel colours and reckoning the pixel density and comparable levels of light. The results show that the approach accurately reveals the contours of panels notwithstanding their shape or the colours of surrounding objects and the environment.