Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation

TL;DR

This paper reviews the application of Graph Neural Networks (GNNs) in Earth Observation, highlighting their potential to handle complex, heterogeneous data and addressing methodological challenges for future research.

Contribution

It provides a comprehensive overview of GNNs in EO, including fundamental knowledge, applications, methodologies, challenges, and comparisons with other architectures.

Findings

GNNs effectively address diverse EO data modalities.

Methodological challenges of GNNs in EO are identified and discussed.

GNNs have potential synergies with transformers in Earth observation tasks.

Abstract

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.