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Hybrid prompting outperforms traditional methods, achieving superior segmentation accuracy for small-scale PV systems with minimal data requirements.
Forget retraining: this method leverages foundation models and test-time adaptation to complete digital surface models with up to 46% lower error than existing approaches.