Label efficient global centralized and distributed photovoltaic 2027 Applied

Accurate global photovoltaic (PV) mapping is essential for renewable energy planning, grid integration, and carbon-neutral transition assessment. AI-based remote sensing segmentation offers an efficient way to update PV inventories, but pixel-wise annotation of PV imagery remains expensive at large scale, especially across distributed rooftop systems and centralized utility-scale plants with different resolutions and geographic backgrounds. A key practical challenge is therefore to achieve high-accuracy PV segmentation with the smallest possible annotation budget under realistic labeling constraints. To address this challenge, we propose a hierarchical CLIP-calibrated active learning (AL) strategy for label-efficient global PV segmentation. Due to the heterogeneous appearance of PV scenes, CLIP is introduced as an external visual-semantic calibrator, and sample acquisition is designed to enhance both coverage and efficiency in PV scene mapping, while also validating the approach through worldwide distributed and centralized PV benchmarks that confirm its robust performance. Furthermore, deployment analysis quantifies the labeling savings achieved for high-accuracy PV mapping.