构建全球最大卫星云去除数据集，助力遥感图像清晰化
AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery
- 构建包含400万张影像的全球多源遥感数据集
- 数据量增30倍时，图像质量指标PSNR提升5.4分
- 涵盖多光谱、雷达及辅助数据，适合遥感与AI研究者
卫星影像中的云层严重影响下游应用。当前云去除研究的一大瓶颈是缺乏全面的基准和足够大且多样化的训练数据集。为此，我们推出了目前最大的公开数据集——AllClear，覆盖23,742个全球分布的感兴趣区域（ROIs），包含总计400万张影像，涵盖多样化土地利用类型。每个ROI包含2022年完整时间序列数据，包括：（1）来自哨兵2号和陆地8/9号的多光谱光学影像，（2）来自哨兵1号的合成孔径雷达（SAR）影像，（3）云掩膜、土地覆盖图等辅助遥感产品。通过基准测试验证了数据集有效性，发现当数据量增加30倍时，PSNR从28.47提升至33.87，并对时间长度和各模态重要性进行了消融分析。该数据集旨在实现地球表面的全面覆盖，推动更优的云去除效果。
原文摘要 · Abstract (English)
Clouds in satellite imagery pose a significant challenge for downstream applications. A major challenge in current cloud removal research is the absence of a comprehensive benchmark and a sufficiently large and diverse training dataset. To address this problem, we introduce the largest public dataset -- $\textit{AllClear}$ for cloud removal, featuring 23,742 globally distributed regions of interest (ROIs) with diverse land-use patterns, comprising 4 million images in total. Each ROI includes complete temporal captures from the year 2022, with (1) multi-spectral optical imagery from Sentinel-2 and Landsat 8/9, (2) synthetic aperture radar (SAR) imagery from Sentinel-1, and (3) auxiliary remote sensing products such as cloud masks and land cover maps. We validate the effectiveness of our dataset by benchmarking performance, demonstrating the scaling law -- the PSNR rises from $28.47$ to $33.87$ with $30\times$ more data, and conducting ablation studies on the temporal length and the importance of individual modalities. This dataset aims to provide comprehensive coverage of the Earth's surface and promote better cloud removal results.
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