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Recently, there has been a growing interest in automatically collecting distributed solar photovoltaic (PV) installation information in smart grid systems, including the quantity and locations of solar PV deployments, as well as their profiling information across a given geospatial region. Most recent approaches are still suffering low detection accuracy due to insufficient sample and principal feature learning when building their models and also separation of rooftop object segmentation and identification during their detection processes. In addition, they cannot report accurate multi-deployment results. To address these problems, we design a new system-SolarDetector⁺, which can automatically and accurately detect and profile distributed solar PV arrays without any extra cost. In essence, SolarDetector⁺first leverages multiple data augmentation techniques, including Cycle-Consistent Adversarial Networks, Latent Diffusion Models, and Generative Adversarial networks, to build a large rooftop satellite imagery dataset (RSID). Then, SolarDetector⁺employs Mask R-convolutional neural networks algorithm to accurately identify rooftop solar PV arrays and learn the detailed installation information for each solar PV array simultaneously. We find that pre-trained SolarDetector⁺yields an average Matthews correlation coefficient of 0.862 to detect solar PV arrays over RSID, which is ∼50% better than the most recent open source detection system—SolarFinder.