用地球模型引导扩散模型，实现极端降尺度下的地表温度超分辨率。
When Earth Foundation Models Meet Diffusion: An Application to Land Surface Temperature Super-Resolution
- 利用地球基础模型提取高分辨率遥感特征，通过交叉注意力指导重建。
- 在32倍降尺度下，超越基线方法，兼顾视觉真实与像素精度。
- 适用于依赖先验地理信息的遥感重建任务，如气象、生态监测。
地表温度（LST）超分辨率对环境监测至关重要，但粗分辨率热观测严重限制了细尺度结构恢复。本文提出地球基础模型引导扩散（EFDiff）框架，用于极端空间降尺度下的超分辨率重建。EFDiff利用Prithvi-EO-2.0地球基础模型将高分辨率多光谱反射率编码为地理空间嵌入，并通过交叉注意力注入去噪网络，指导从高度退化观测中重建细尺度结构。研究了两种变体：EFDiff-ε与EFDiff-x₀，分别在感知真实性和像素级保真度之间取得互补权衡。在包含242,416个时空配准的陆地卫星热-反射率图像块的全球基准上，评估了32倍尺度差距下的性能。结果表明，EFDiff始终优于基线方法，且地球基础模型的交叉注意力条件比多通道拼接更有效。尽管本文聚焦于LST超分辨率，该框架可广泛应用于其他依赖预训练地理表示的遥感生成重建问题。
原文摘要 · Abstract (English)
Land surface temperature (LST) super-resolution is important for environmental monitoring. However, it remains challenging as coarse thermal observations severely underdetermine fine-scale structure. In this paper, we propose Earth Foundation Model-guided Diffusion (EFDiff), a novel framework for super-resolution under extreme spatial degradation. EFDiff uses the Prithvi-EO-2.0 Earth foundation model to encode high-resolution multispectral reflectance into geospatial embeddings, which are injected into the denoising network via cross-attention to guide fine-scale reconstruction from highly degraded observations. We study two variants, EFDiff-$ε$ and EFDiff-$x_0$, which offer complementary trade-offs between perceptual realism and pixel-level fidelity. We evaluate EFDiff under an extreme $32\times$ scale gap using a globally diverse benchmark comprising 242,416 co-registered Landsat thermal-reflectance patches. Results show that EFDiff consistently outperforms baseline methods and that cross-attention conditioning by EFM is more effective than HLS channel concatenation. Although we present EFDiff in the context of LST super-resolution, the framework is broadly applicable to remote sensing problems in which pretrained geospatial representations can guide generative reconstruction.
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