Remote sensing foundation models largely break away from the traditional paradigm of designing task-specific models, offering greater scalability across multiple tasks. However, they face challenges such as low computational efficiency and limited interpretability, especially when dealing with large-scale remote sensing images. To overcome these, we draw inspiration from heat conduction, a physical process modeling local heat diffusion. Building on this idea, we are the first to explore the potential of using the parallel computing model of heat conduction to simulate the local region correlations in high-resolution remote sensing images, and introduce RS-vHeat, an efficient multi-modal remote sensing foundation model. Specifically, RS-vHeat 1) applies the Heat Conduction Operator (HCO) with a complexity of $O(N^{1.5})$ and a global receptive field, reducing computational overhead while capturing remote sensing object structure information to guide heat diffusion; 2) learns the frequency distribution representations of various scenes through a self-supervised strategy based on frequency domain hierarchical masking and multi-domain reconstruction; 3) significantly improves efficiency and performance over state-of-the-art techniques across 4 tasks and 10 datasets. Compared to attention-based remote sensing foundation models, we reduce memory usage by 84\%, FLOPs by 24\% and improves throughput by 2.7 times. The code will be made publicly available.

翻译：遥感基础模型在很大程度上摆脱了设计任务特定模型的传统范式，为跨多个任务提供了更强的可扩展性。然而，它们面临着计算效率低和可解释性有限等挑战，尤其是在处理大规模遥感图像时。为了克服这些挑战，我们从热传导这一模拟局部热扩散的物理过程中获得灵感。基于这一思想，我们首次探索了利用热传导的并行计算模型来模拟高分辨率遥感图像中局部区域相关性的潜力，并引入了RS-vHeat，一个高效的多模态遥感基础模型。具体而言，RS-vHeat 1) 应用了具有$O(N^{1.5})$复杂度和全局感受野的热传导算子，在捕捉遥感对象结构信息以引导热扩散的同时降低了计算开销；2) 通过一种基于频域分层掩码和多域重建的自监督策略，学习各种场景的频率分布表示；3) 在4个任务和10个数据集上，相比最先进技术，显著提升了效率和性能。与基于注意力的遥感基础模型相比，我们将内存使用降低了84%，FLOPs减少了24%，并将吞吐量提高了2.7倍。代码将公开提供。