Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation

• URL: http://arxiv.org/abs/2411.03223v2
• Date: Wed, 06 Nov 2024 09:10:46 GMT
• Title: Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation
• Authors: Shan Zhao, Zhaiyu Chen, Zhitong Xiong, Yilei Shi, Sudipan Saha, Xiao Xiang Zhu,
• Abstract summary: Graph Neural Networks (GNNs) emerge as an important innovation, propelling DL into the non-Euclidean domain. GNNs can effectively tackle the challenges posed by diverse modalities, multiple sensors, and the heterogeneous nature of Earth Observation data. This paper explores 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.
• Score: 26.397297480169858
• License:
• 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.

Related papers

• A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges [75.37448213291668]
  This paper systematically reviews existing Graph Neural Networks (GNNs) We first highlight the four key challenges faced by existing GNNs, paving the way for our exploration of real-world GNN models.
  arXiv  Detail & Related papers  (2024-03-07T13:10:37Z)
• From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond [32.290102818872526]
  Graph neural networks (GNNs) have demonstrated significant promise in modelling data and have been widely applied in various fields of interest. We provide the first systematic and comprehensive review of studies that leverage the continuous perspective of GNNs.
  arXiv  Detail & Related papers  (2023-10-16T06:57:24Z)
• Information Flow in Graph Neural Networks: A Clinical Triage Use Case [49.86931948849343]
  Graph Neural Networks (GNNs) have gained popularity in healthcare and other domains due to their ability to process multi-modal and multi-relational graphs. We investigate how the flow of embedding information within GNNs affects the prediction of links in Knowledge Graphs (KGs) Our results demonstrate that incorporating domain knowledge into the GNN connectivity leads to better performance than using the same connectivity as the KG or allowing unconstrained embedding propagation.
  arXiv  Detail & Related papers  (2023-09-12T09:18:12Z)
• A Survey on Explainability of Graph Neural Networks [4.612101932762187]
  Graph neural networks (GNNs) are powerful graph-based deep-learning models. This survey aims to provide a comprehensive overview of the existing explainability techniques for GNNs.
  arXiv  Detail & Related papers  (2023-06-02T23:36:49Z)
• The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey [17.746899445454048]
  Graph Neural Networks (GNNs) are an emerging research field. GNNs can be applied to various domains including recommendation systems, computer vision, natural language processing, biology and chemistry. We aim to fill this gap by summarizing and categorizing important methods and techniques for large-scale GNN solutions.
  arXiv  Detail & Related papers  (2023-05-23T09:22:33Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks [51.42338058718487]
  Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs. We propose EvenNet, a spectral GNN corresponding to an even-polynomial graph filter.
  arXiv  Detail & Related papers  (2022-05-27T10:48:14Z)
• Trustworthy Graph Neural Networks: Aspects, Methods and Trends [115.84291569988748]
  Graph neural networks (GNNs) have emerged as competent graph learning methods for diverse real-world scenarios. Performance-oriented GNNs have exhibited potential adverse effects like vulnerability to adversarial attacks. To avoid these unintentional harms, it is necessary to build competent GNNs characterised by trustworthiness.
  arXiv  Detail & Related papers  (2022-05-16T02:21:09Z)
• Federated Graph Neural Networks: Overview, Techniques and Challenges [16.62839758251491]
  Graph neural networks (GNNs) have received significant research attention. As societies become increasingly concerned with data privacy, GNNs face the need to adapt to this new normal. This has led to the rapid development of federated graph neural networks (FedGNNs) research in recent years.
  arXiv  Detail & Related papers  (2022-02-15T09:05:35Z)
• Incorporating Symbolic Domain Knowledge into Graph Neural Networks [18.798760815214877]
  Deep neural networks specifically developed for graph-structured data (Graph-based Neural Networks, or GNNs) We investigate this aspect of GNNs empirically by employing an operation we term "vertex-enrichment" and denote the corresponding GNNs as "VEGNNs"
  arXiv  Detail & Related papers  (2020-10-23T16:22:21Z)
• Graph Neural Networks for Motion Planning [108.51253840181677]
  We present two techniques, GNNs over dense fixed graphs for low-dimensional problems and sampling-based GNNs for high-dimensional problems. We examine the ability of a GNN to tackle planning problems such as identifying critical nodes or learning the sampling distribution in Rapidly-exploring Random Trees (RRT) Experiments with critical sampling, a pendulum and a six DoF robot arm show GNNs improve on traditional analytic methods as well as learning approaches using fully-connected or convolutional neural networks.
  arXiv  Detail & Related papers  (2020-06-11T08:19:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.