Filling cloudy pixels in multispectral satellite imagery is essential for
accurate data analysis and downstream applications, especially for tasks which
require time series data. To address this issue, we compare the performance of
a foundational Vision Transformer (ViT) model with a baseline Conditional
Generative Adversarial Network (CGAN) model for missing value imputation in
time series of multispectral satellite imagery. We randomly mask time series of
satellite images using real-world cloud masks and train each model to
reconstruct the missing pixels. The ViT model is fine-tuned from a pretrained
model, while the CGAN is trained from scratch. Using quantitative evaluation
metrics such as structural similarity index and mean absolute error as well as
qualitative visual analysis, we assess imputation accuracy and contextual
preservation.

通过比较基础的 Vision Transformer (ViT) 模型与基准的条件生成对抗网络 (CGAN) 模型在时间序列的多光谱卫星图像上进行缺失值插补的性能，我们评估了插补准确性和语境保留，使用了结构相似性指数和绝对均值误差等定量评估指标以及定性的视觉分析。

透过云层看清楚：使用 Prithvi 基础模型填补云间隙

Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation  Model

Solar Photovoltaic (PV) technology is increasingly recognized as a pivotal
solution in the global pursuit of clean and renewable energy. This technology
addresses the urgent need for sustainable energy alternatives by converting
solar power into electricity without greenhouse gas emissions. It not only
curtails global carbon emissions but also reduces reliance on finite,
non-renewable energy sources. In this context, monitoring solar panel farms
becomes essential for understanding and facilitating the worldwide shift toward
clean energy. This study contributes to this effort by developing the first
comprehensive global dataset of multispectral satellite imagery of solar panel
farms. This dataset is intended to form the basis for training robust machine
learning models, which can accurately map and analyze the expansion and
distribution of solar panel farms globally. The insights gained from this
endeavor will be instrumental in guiding informed decision-making for a
sustainable energy future. this https URL

开发了首个全球多光谱卫星图像太阳能电池板农场的综合数据集，用于训练强大的机器学习模型，准确地绘制和分析全球太阳能电池板农场的扩展和分布，为可持续能源未来的明智决策提供重要指导。