Weakly-semi supervised extraction of rooftop photovoltaics from high-resolution images based on segment anything model and class activation map

Ruiqing Yang, Guojin He, Ranyu Yin, Guizhou Wang, Zhaoming Zhang, Tengfei Long and Yan Peng

Applied Energy, 2024, vol. 361, issue C, No S0306261924003477

Abstract: Accurate extraction of rooftop photovoltaic from high-resolution remote sensing imagery is pivotal for propelling green energy planning and development. Conventional deep learning techniques often require labor-intensive pixel-level annotations, presenting substantial limitations. To overcome this hurdle, we introduce an innovative weakly-semi supervised segmentation framework that strategically employs both a “Segment Anything Model” (SAM) and “Class Activation Maps” (CAM) to produce high-precision and efficient pseudo-labels. To manage the unique error characteristics arising from the fusion of SAM and CAM-derived pseudo-labels, our framework incorporates semi-supervised learning algorithms and a boundary-aware loss function. We conducted experiments on a publicly available dataset, yielding an Intersection over Union (IoU) rate of 74% and an F1 score of 84%. Remarkably, this performance reaches approximately 88% of the benchmark established by fully-supervised methods. Our ablation studies further substantiate the effectiveness of our framework, thereby carving out a new trajectory in the realm of weakly-supervised segmentation. The study also delineates certain limitations, particularly focusing on the granularity of SAM-based segmentation and its implications for large-scale photovoltaic installations. Our methodology not only elevates segmentation accuracy but also substantially alleviates the manual labor required for dataset preparation.

Keywords: Segment anything model; Weakly supervised learning; Semi-supervised learning; Deep learning (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.apenergy.2024.122964

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