Solar photovoltaic panel detection data

How to detect photovoltaic cells in aerial images?

Recognition of photovoltaic cells in aerial images with Convolutional Neural Networks (CNNs). Object detection with YOLOv5 models and image segmentation with Unet++, FPN, DLV3+ and PSPNet.

Do solar panels have object detection models?

Reports of solar panel installations have been supplemented with object detection models developed and used on openly available aerial imagery, a type of imagery collected by aircraft or drones and limited by cost, extent, and geographic location.

How can spectroscopy be used to identify solar panels?

To identify, e.g., PV modules in imaging spectroscopy data, the material specific absorption characteristics can be replicated through different indices [ 3 ]. Czirjak [ 18] found that solar panels share a similar spectral signature allowing for detection, regardless of the manufacturer or construction of the modules.

Should imaging spectroscopy data be used for PV identification?

This study aims to create greater awareness of the potential importance of imaging spectroscopy data for PV identification. As a physics-based approach, it is robust, transferable, and can provide data on PV coverage on a regional or global scale in short time.

Can imaging spectroscopy data detect PV material as pure pixels?

Moreover, the spatial resolution of the imaging spectroscopy data should be sufficient to detect PV material as pure pixels. This study aims to create greater awareness of the potential importance of imaging spectroscopy data for PV identification.

Can physics be used to detect PV modules using imaging spectroscopy data?

This physics-based approach was validated against a large database collected from multiple platforms (laboratory measurements, airborne imaging spectroscopy data), thus providing a robust, transferable and applicable way to detect PV modules using imaging spectroscopy data.