SPEAR NeXT Causal Latent Forecasting Across Multiple Horizons for Spectral Temporal Earth Representation Learning

2026-09-15

AI Summary

为了解决地球观测中时间信息学习的问题，SPEAR NeXT模型通过因果掩码Transformer预测多时序潜在状态，利用旋转位置嵌入和年月嵌入来表示相对时间和季节性。

Abstract

Earth observation is inherently dynamic, yet temporal information in many foundation models is learned through reconstruction, invariance, or retrospective sequence summarization. SPEAR NeXT is introduced as a compact pixel-wise multimodal spectral temporal foundation model in which temporal self supervision is formulated as past only, multi horizon latent Earth state prediction. Instantaneous states are first encoded by the pretrained SPEAR model from optical, radar, and environmental observations into compact 32 dimensional embeddings. Their temporal evolution is then modeled by a causally masked Trans former that predicts multiple future latent states from pre ceding observations. Relative temporal order is represented using Rotary Position Embeddings, while month and year embeddings encode seasonal phase and interannual con text.

Problem

• Earth observation
• temporal information
• causal latent forecasting
• spectral temporal representation

Innovation

• causally masked Transformer
• Rotary Position Embeddings
• multimodal spectral temporal foundation model