Land Deformation Prediction via Slope-Aware Graph Neural Networks

Authors

  • Fan Zhou School of Information and Software Engineering, University of Electronic Science and Technology of China
  • Rongfan Li School of Information and Software Engineering, University of Electronic Science and Technology of China
  • Goce Trajcevski Iowa State University
  • Kunpeng Zhang University of Maryland

Keywords:

Environmental Sustainability

Abstract

We introduce a slope-aware graph neural network (SA-GNN) to leverage continuously monitored data and predict the land displacement. Unlike general GNNs tackling tasks in the plain graphs, our method is capable of generalizing 3D spatial knowledge from InSAR point clouds. Specifically, we structure of the land surface, while preserving the spatial correlations among adjacent points. The point cloud can then be efficiently converted to a near-neighbor graph where general GNN methods can be applied to predict the displacement of the slope surface. We conducted experiments on real-world datasets and the results demonstrate that SA-GNN outperforms existing 3D CNN and point GNN methods.

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Published

2021-05-18

How to Cite

Zhou, F., Li, R., Trajcevski, G., & Zhang, K. (2021). Land Deformation Prediction via Slope-Aware Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15033-15040. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17764

Issue

Section

AAAI Special Track on AI for Social Impact