In this study, a semantic segmentation network called HCT-Net, combined with the hybrid neural networks and the swarm intelligence optimization algorithms, is designed to segment
We provide a remote sensing derived dataset for large-scale ground-mounted photovoltaic (PV) power stations in China of 2020, which has high spatial resolution of 10 meters.
According to the PV panel defect detection task, the structure of YOLOv5 is improved and innovated in this paper. Firstly, the semantic depth information of PV panel images is obtained using the
Therefore, this study focuses on the fine-grained extraction of PV panels within photovoltaic power plants, aiming to overcome the limitations of boundary-based segmentation.
Our resultant PV panel map provides a detailed dataset for renewable layouts, ecological assessments, and the energy-related Sustainable Development Goals (SDGs).
Accurate monitoring and assessment of sand–dust accumulation levels are essential for optimizing cleaning schedules of photovoltaic systems in dusty regions. This article proposes an
In this study, a semantic segmentation network called HCT-Net, combined with the hybrid neural networks and the swarm intelligence optimization algorithms, is designed to segment
We optimized the placement and capacity of PV and wind power plants in our model driven by geospatial data (Supplementary Method 1), including land cover, solar radiation, wind speed, surface
PV panel surface-defect detection dataset. Contribute to CCNUZFW/PV-Multi-Defect development by creating an account on GitHub.
To tackle the challenge of modeling PV panels with diverse structures, we propose a coupled U-Net and Vision Transformer model named TransPV for refining PV semantic segmentation.
Compared to existing datasets, it provides better precision and spatial detail, showing global PV growth of over 60% between 2019 and 2022, with developing countries leading the increase.
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