提出新框架，让全球空气质量预测跨越区域限制。
Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting
- 用环境属性构建通用站点标识，动态生成稀疏拓扑。
- 在7800个站点上实现领先精度，速度提升近10倍。
- 适合数据稀疏地区预测，尤其关注全球环境监测者。
全球空气质量预测面临极端空间异质性及现有模型在未见区域泛化能力差的问题。为此，我们提出OmniAir，一种面向全球站点级预测的语义拓扑学习框架。通过将不变的物理环境属性编码为可泛化的站点标识，并动态构建自适应稀疏拓扑，该方法有效捕捉了分布不均的全球网络中长距离非欧几里得相关性和物理扩散模式。我们进一步构建了覆盖全球超过7,800个站点的WorldAir大规模数据集。大量实验表明，OmniAir在18个基线模型中达到最优性能，兼具高效率与可扩展性，推理速度接近现有模型的10倍，同时显著缩小了数据稀疏区域的监测差距。
原文摘要 · Abstract (English)
Global air quality forecasting grapples with extreme spatial heterogeneity and the poor generalization of existing transductive models to unseen regions. To tackle this, we propose OmniAir, a semantic topology learning framework tailored for global station-level prediction. By encoding invariant physical environmental attributes into generalizable station identities and dynamically constructing adaptive sparse topologies, our approach effectively captures long-range non-Euclidean correlations and physical diffusion patterns across unevenly distributed global networks. We further curate WorldAir, a massive dataset covering over 7,800 stations worldwide. Extensive experiments show that OmniAir achieves state-of-the-art performance against 18 baselines, maintaining high efficiency and scalability with speeds nearly 10 times faster than existing models, while effectively bridging the monitoring gap in data-sparse regions.
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