Chronosphere: Space-Time Tessellation of Local Climate Experts

2026-09-18

AI Summary

Chronosphere通过自适应的空间时间镶嵌和局部基函数，解决了地理表示学习中环境过程复杂度建模的问题，提高了气候数据的时空表示能力。

Abstract

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.

Problem

• spatio-temporal
• geographic representation learning
• environmental processes
• location encoders
• tessellation

Innovation

• spatio-temporal neural field
• adaptive tessellation
• learnable sites
• local basis functions
• climate representation