Land Deformation Prediction via Slope-Aware Graph Neural Networks

Fan Zhou, Rongfan Li, Goce Trajcevski, Kunpeng Zhang

AAAI 2021 pp. 15033-15040

doi:10.1609/AAAI.V35I17.17764 /aaai/2021/zhou2021aaai-land/

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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Cite

Text

Zhou et al. "Land Deformation Prediction via Slope-Aware Graph Neural Networks." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I17.17764

Markdown

[Zhou et al. "Land Deformation Prediction via Slope-Aware Graph Neural Networks." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/zhou2021aaai-land/) doi:10.1609/AAAI.V35I17.17764

BibTeX

@inproceedings{zhou2021aaai-land,
  title     = {{Land Deformation Prediction via Slope-Aware Graph Neural Networks}},
  author    = {Zhou, Fan and Li, Rongfan and Trajcevski, Goce and Zhang, Kunpeng},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15033-15040},
  doi       = {10.1609/AAAI.V35I17.17764},
  url       = {https://mlanthology.org/aaai/2021/zhou2021aaai-land/}
}