Chronosphere：局部气候专家的时空镶嵌

中文摘要 AI 辅助

我们引入了Chronosphere，一种学习气候表征的时空神经场。地理表征学习中的一个核心挑战是建模环境过程，其空间和时间复杂性变化很大。然而，现有的位置编码器通常在所有地方固定单一细节级别。诸如球谐函数之类的全局基函数在空间和时间上均匀分布容量。局部基函数仅解析预定义区域。学习到的镶嵌会自适应，但在表示较高频率时效率低下。Chronosphere统一了这些方法，将时空环面$S^2\ imes S^1$上可学习位点的自适应镶嵌与共享的局部基函数库配对。容量放置的位置以及每个区域携带的细节量都适应数据，跨越空间和时间。经过训练以重建气候学，Chronosphere在空间和时间任务上匹配或领先于最先进的位置编码器，在空间和时间迁移下获得最大增益。

英文摘要

We introduce Chronosphere, a spatio-temporal neural field that learns representations of climate. A central challenge in geographic representation learning is modeling environmental processes whose spatial and temporal complexity varies widely. Yet existing location encoders typically fix a single level of detail everywhere. Global bases such as spherical harmonics spread capacity uniformly across space and time. Localized bases resolve only predefined regions. Learned tessellations adapt, but are inefficient at representing higher frequencies. Chronosphere unifies these approaches, pairing an adaptive tessellation of learnable sites on the spacetime torus $S^2\times S^1$ with a shared bank of local basis functions. Both where capacity is placed and how much detail each region carries adapt to the data, across space and time. Trained to reconstruct climatology, Chronosphere matches or leads state-of-the-art location encoders across spatial and temporal tasks, with the largest gains under spatial and temporal transfer.

发表机构

• Washington University in St. Louis（圣路易斯华盛顿大学）
• Taylor Geospatial Institute（泰勒地理空间研究所）

机构由 AI 辅助整理，请以论文原文为准。