# Detection and Optimization of Photovoltaic Arrays' Tilt Angles Using Remote Sensing Data
## 1 Introduction  
Solar energy harvesting plays a significant role in renewable energy production, particularly in the past decade, due to the decreasing costs of photovoltaic (PV) modules, green energy initiatives, and the increased nominal power output of PV technologies [1]. Modules are a popular choice for solar energy harvesting, suitable not just for large-scale PV systems but also for smaller installations on building rooftops in urban environments [2]. Over the past few years, a number of simulation and machine learning methods have been developed to accurately estimate the viability of rooftops for PV systems' placement. The increased availability in remote sensing data, such as Light Detection and Ranging (LiDAR), provides a more accurate 3D representation of building rooftops and their surroundings. This helps to better incorporate the effects of shading, especially in dense urban contexts. Airborne LiDAR scanning involves an aircraft emitting a laser pulse, which is reflected back to the aircraft. The time taken for the pulse to travel allows the estimation of the distance to the surface, resulting in a 3D point cloud [3]. Over the years, variousmethods have been developed that utilize point cloud data derived from LiDAR or an unmanned aerial vehicle (UAV) to assess the solar or PV potential of 3D surfaces using solar irradiance simulation or deep learning approaches [4; 5; 6; 7]. Moreover, newer methods leverage 3D surface information for the optimal placement of hypothetical PV systems [8; 9]. In addition to airborne LiDAR data, very high-resolution (VHR) satellite or aerial imagery, with a resolution of less than 1 m\({}^{2}\) per pixel, is already widely available for many geographic areas. Consequently, methods have been developed to identify the installations of existing PV systems in both lower and higher resolutions [10]. When dealing with PV detection in VHR, there can be three levels of granularity involved, as seen in Figure 1, where an entire PV system can be detected (Figure 1b), individual PV arrays (Figure 1c) or individual PV modules (Figure 1d). In recent years, various methods have been developed to perform semantic segmentation using convolutional neural networks (CNNs) [11; 12]. [PERSON] et al. [13] have demonstrated that classical machine learning delivers high-quality results when dealing with low-resolution satellite imagery, while [PERSON] et al. [14] have shown that the detection of arrays and modules in VHR is more challenging due to class imbalance, non-concentrated distribution, homogeneous texture, and heterogeneous color features. Hence, more recent works primarily employ U-shaped encoder-decoder deep learning architectures such as classical U-Net derivations [11]; a modified CNN pipeline with U-Net [15] using ResNet50 encoding [16]; a cross-learning-driven U-Net [17]; a modified U-Net with VGG16 encoding [18]; a self-paced residual aggregated network [19]; a split-attention network combined with a dual-attention network [20]; coarse prediction with fine optimization [21]; various U-Nets with EfficientNet [22], SE-ResNeXt [23], and InceptionResNet [24] backbone encoders [25]; and recently using a vision Transformer [26]. All of the aforementioned methods focus on the detection of entire PV systems (low granularity) or arrays (medium granularity). Only a few attempts are made in the literature for the detection of individual modules [27], where the best results were obtained utilizing scarcely available remote sensing data, such as hyperspectral [28] or thermal imagery [29], without considering optical VHR imagery or LiDAR data. In recent years, methods have also measured the expected capacity of existing PV system installations on a larger scale [30] based on remote sensing data and estimated their potential energy output. [PERSON] et al. [31] detected arrays using the DeepLab-V3 semantic segmentation model [32], and estimated PV capacity based on the area of the array. They assumed each module area to be 6 m\({}^{2}\) for an output of 1 kWp (kW peak).