Understanding rooftop PV panel semantic segmentation of satellite and aerial images for better using machine learning
Understanding rooftop PV panel semantic segmentation of satellite and aerial images for better using machine learning
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DOI：
10.1016/j.adapen.2021.100057
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发表时间：
2021-11-19
影响因子：
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通讯作者：
Yan, Jinyue
中科院分区：
文献类型：
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作者：
Li, Peiran;Zhang, Haoran;Yan, Jinyue
光伏产业的繁荣和光伏应用的增加要求基于准确和更新的装机容量数据进行更好的规划。与人工统计方法耗时耗力相比，利用卫星/航空图像估算现有光伏装机容量提供了一种成本效益高、数据一致的新方法。之前的研究调查了利用机器学习技术从图像中分割光伏电池板的可行性。然而，由于光伏面板语义分割的特殊特点，机器学习工具的设计和应用需要仔细考虑问题的表述、数据质量和模型的可解释性。本文从计算机视觉的角度研究了光伏面板语义分割的特点。结果表明，光伏板图像数据具有以下几个特点：分类不平衡程度高，分布不集中；质地均匀，颜色不均匀；显著的分辨率阈值，实现了有效的语义分割。此外，本文还从计算机视觉最新解决方案的角度，针对每个观察到的特征，对数据的获取和模型的设计提出了建议，可以为未来光伏面板语义分割的改进提供帮助。
The photovoltaic (PV) industry boom and increased PV applications call for better planning based on accurate and updated data on the installed capacity. Compared with the manual statistical approach, which is often timeconsuming and labor-intensive, using satellite/aerial images to estimate the existing PV installed capacity offers a new method with cost-effective and data-consistent features. Previous studies investigated the feasibility of segmenting PV panels from images involving machine learning technologies. However, due to the particular characteristics of PV panel semantic-segmentation, the machine learning tools need to be designed and applied with careful considerations of the issue formulation, data quality, and model explainability. This paper investigated the characteristics of PV panel semantic-segmentation from the perspective of computer vision. The results reveal that the PV panel image data has several specific characteristics: highly class-imbalance and non-concentrated distribution; homogeneous texture and heterogenous color features; and the notable resolution threshold for effective semantic-segmentation. Moreover, this paper provided recommendations for data obtaining and model design, aiming at each observed character from the viewpoints of recent solutions in computer vision, which can be helpful for future improvement of the PV panel semantic-segmentation.