用图神经网络分析地球观测数据，突破传统网格限制。
Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation
- 将地球观测数据构建成图结构，用GNN处理非欧几里得数据
- 在气象、灾害、农业等领域展现有效应用潜力
- 适合处理多源异构数据，对遥感研究者有参考价值
地球观测（EO）数据分析因深度学习而显著变革，但多数应用局限于网格状数据结构。图神经网络（GNN）作为重要创新，推动深度学习进入非欧几里得领域，能有效应对多元模态、多传感器及数据异质性挑战。本文首先介绍GNN基础理论，梳理EO领域的通用问题，并探讨GNN在天气气候分析、灾害管理、空气质量监测、农业、土地覆盖分类、水文过程建模和城市建模等地球系统科学问题中的广泛应用。文中解释了采用GNN的合理性，说明了图结构构建方法与针对不同任务的架构设计。同时指出实现中的方法论挑战，并提出可能的解决方向。尽管GNN并非万能解法，本文还对比了其与Transformer等主流架构的差异，分析了潜在协同效应。
原文摘要 · Abstract (English)
Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures. Graph Neural Networks (GNNs) emerge as an important innovation, propelling DL into the non-Euclidean domain. Naturally, GNNs can effectively tackle the challenges posed by diverse modalities, multiple sensors, and the heterogeneous nature of EO data. To introduce GNNs in the related domains, our review begins by offering fundamental knowledge on GNNs. Then, we summarize the generic problems in EO, to which GNNs can offer potential solutions. Following this, we explore a broad spectrum of GNNs' applications to scientific problems in Earth systems, covering areas such as weather and climate analysis, disaster management, air quality monitoring, agriculture, land cover classification, hydrological process modeling, and urban modeling. The rationale behind adopting GNNs in these fields is explained, alongside methodologies for organizing graphs and designing favorable architectures for various tasks. Furthermore, we highlight methodological challenges of implementing GNNs in these domains and possible solutions that could guide future research. While acknowledging that GNNs are not a universal solution, we conclude the paper by comparing them with other popular architectures like transformers and analyzing their potential synergies.
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