Classification and segmentation of five photovoltaic types based on instance segmentation for generating more refined photovoltaic data

Photovoltaic types and spatial information are indispensable for power generation estimation, environmental impact assessment and photovoltaic policy formulation. However, previous studies have predominantly focused on extracting the spatial information of photovoltaics, overlooking the significance of photovoltaic types classification. Moreover, existing research on the classification and segmentation of some novel photovoltaic types is limited. With the widespread adoption of new photovoltaic technologies, the limitations of the data obtained from these studies have become increasingly apparent due to the lack of information on photovoltaic types. To tackle the challenge of the diversification and complexity of photovoltaics, we propose a photovoltaic classification and segmentation network (PV-CSN). This network can automatically classify and segment photovoltaics, providing segmentation results that include information about photovoltaic types. In addition, to enhance network performance, PV-CSN incorporates the receptive field enhancement segment module, BC-PAN-FPN structure, and efficient multi-scale attention module. The experimental results demonstrate the PV-CSN's capability to accurately classify and segment five types of photovoltaics: ground fixed-tilt photovoltaics, ground single-axis tracking photovoltaics, roof photovoltaics, floating water photovoltaics, and stationary water photovoltaics. The Mask-mAP and Box-mAP of this network reach 0.915 and 0.916 respectively, remaining competitive compared to the most advanced instance segmentation networks developed in recent years. These outcomes highlight the superiority of PV-CSN in improving the accuracy of photovoltaic classification and segmentation, as well as solving the identification of new photovoltaics, which provides a promising solution for photovoltaic classification and segmentation tasks.