用多模态条件卷积提升地表温度超分辨率，开源完整工具链。
MoCoLSK: Modality Conditioned High-Resolution Downscaling for Land Surface Temperature
- 通过模态条件投影动态融合多源遥感数据
- 在多个数据集上实现比现有方法更高的地表温度预测精度
- 适合遥感、环境建模及深度学习研究者使用
地表温度（LST）是环境研究的关键参数，但受卫星遥感时空权衡限制，直接获取高空间分辨率的LST数据仍具挑战。引导式下采样成为替代方案，但现有方法常忽略空间非平稳性，且缺乏深度学习开源生态。本文提出模态条件大选择核网络（MoCoLSK），通过模态条件投影动态融合多模态数据，实现可变感受野与多模态特征融合的统一，显著提升LST预测精度。此外，我们构建了GrokLST项目，包含GrokLST数据集、高分辨率基准和基于PyTorch的GrokLST工具包，集成MoCoLSK及40余种先进方法。大量实验验证了MoCoLSK在捕捉多光谱数据复杂依赖关系与细微变化上的有效性，优于现有方法。代码、数据集与工具包已公开于https://github.com/GrokCV/GrokLST。
原文摘要 · Abstract (English)
Land Surface Temperature (LST) is a critical parameter for environmental studies, but directly obtaining high spatial resolution LST data remains challenging due to the spatio-temporal trade-off in satellite remote sensing. Guided LST downscaling has emerged as an alternative solution to overcome these limitations, but current methods often neglect spatial non-stationarity, and there is a lack of an open-source ecosystem for deep learning methods. In this paper, we propose the Modality-Conditional Large Selective Kernel (MoCoLSK) Network, a novel architecture that dynamically fuses multi-modal data through modality-conditioned projections. MoCoLSK achieves a confluence of dynamic receptive field adjustment and multi-modal feature fusion, leading to enhanced LST prediction accuracy. Furthermore, we establish the GrokLST project, a comprehensive open-source ecosystem featuring the GrokLST dataset, a high-resolution benchmark, and the GrokLST toolkit, an open-source PyTorch-based toolkit encapsulating MoCoLSK alongside 40+ state-of-the-art approaches. Extensive experimental results validate MoCoLSK's effectiveness in capturing complex dependencies and subtle variations within multispectral data, outperforming existing methods in LST downscaling. Our code, dataset, and toolkit are available at https://github.com/GrokCV/GrokLST.
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