Abstract
Clouds are a major obstacle in Earth observation, limiting the usability and reliability of critical remote sensing applications such as fire disaster response, urban heat island monitoring, and snow and ice cover mapping. Therefore, the ability to detect clouds 24/7 is of paramount importance. While visible and near-infrared bands are effective for daytime cloud detection, their dependence on solar illumination makes them unsuitable for nighttime monitoring. In contrast, thermal infrared (TIR) imagery plays a crucial role in detecting clouds at night, when sunlight is absent. Due to their generally lower temperatures, clouds emit distinct thermal signatures that are detectable in TIR bands. Despite this, accurate nighttime cloud detection remains challenging due to limited spectral information and the typically lower spatial resolution of TIR imagery. To address these challenges, we present TIRAuxCloud, a multi-modal dataset centered around thermal spectral data to facilitate cloud segmentation under both daytime and nighttime conditions. The dataset comprises a unique combination of multispectral data (TIR, optical, and near-infrared bands) from Landsat and VIIRS, aligned with auxiliary information layers. Elevation, land cover, meteorological variables, and cloud-free reference images are included to help reduce surface-cloud ambiguity and cloud formation uncertainty. To overcome the scarcity of manual cloud labels, we include a large set of samples with automated cloud masks and a smaller manually annotated subset to further evaluate and improve models. Comprehensive benchmarks are presented to establish performance baselines through supervised and transfer learning, demonstrating the dataset's value in advancing the development of innovative methods for day and night time cloud detection.
Abstract (translated)
云是地球观测中的主要障碍，限制了关键遥感应用（如火灾灾害响应、城市热岛监测和雪冰覆盖制图）的可用性和可靠性。因此，24/7全天候检测云的能力至关重要。虽然可见光和近红外波段在白天有效用于检测云层，但它们依赖于太阳光照的特点使它们不适合夜间监控。相比之下，在没有阳光的情况下，热红外（TIR）影像在夜间云层检测中发挥着关键作用。由于通常温度较低，云层在TIR波段表现出独特的热特征，这些特征可以被探测到。然而，准确的夜间云层检测仍然具有挑战性，因为可用的光谱信息有限，并且TIR图像的空间分辨率一般较低。 为了解决这些问题，我们提出了一个以热光谱数据为中心的多模态数据集 TIRAuxCloud，旨在促进在白天和夜晚条件下的云分割。该数据集结合了来自 Landsat 和 VIIRS 的多光谱数据（包括 TIR、光学和近红外波段），并配有辅助信息层。地形高度、土地覆盖、气象变量以及无云参考图像被包含进来以帮助减少地表与云之间的混淆，并降低云形成不确定性。 为了克服手动标注的不足，我们提供了一大批带有自动云掩模的数据样本，以及一个较小的手动注释子集，用于进一步评估和改进模型。通过监督学习和迁移学习建立了全面基准测试，展示了该数据集在促进日间与夜间云层检测创新方法开发方面的价值。
URL
https://arxiv.org/abs/2602.21905
