General generative AI-based image augmentation method for robust rooftop PV segmentation

Rooftop photovoltaic (PV) segmentation based on remote sensing images is highly applied in solar potential assessment and prediction. Still, such methods often feature dataset limitations of PV data, poor robustness, and are non-generalizable. General Generative AI eliminates the need for pre-training emerging to improve the sample diversity and algorithm robustness and generalizability of the segmentation. This paper designs a PV sample generation method based on the generative model, which leverages the text-guided stable diffusion inpainting model to augment the PV dataset and generate massive multi-background rooftop PV panel samples. The real and generated samples are mixed in different proportions to form a new training set for ablation experiments. Results show that a small number of real datasets mixed with generated data could reach a high relative IoU and Precision value. In small sample learning, the generated data achieves similar effects as real data during the segmenting process even better than without generated data. It demonstrates that the generated datasets outperform traditionally augmented data and that the manual text prompts are tested more accurately than ChatGPT-generated ones. This study highlights the efficiency and robustness of generated datasets in PV segmentation tasks and moves beyond the constraints of remote sensing data acquisition and limited data diversity. Further, it would facilitate large-scale assessments of the urban PV potential for urban planners and policymakers using an efficient and low-cost method.