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Arxiv
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Learning Geometric Invariant Features for Classification of Vector Polygons with Graph Message-passing Neural Network
- Geometric shape classification of vector polygons is a challenging task in spatial analysis.
- This study introduces a graph-based representation of vector polygons and proposes a graph message-passing framework, PolyMP, and its variant, PolyMP-DSC.
- PolyMP aims to learn more expressive and robust latent representations of polygons by capturing self-looped graph information hierarchically.
- The framework focuses on learning geometric-invariant features for polygon shape classification.
- Extensive experiments demonstrate that combining a permutation-invariant graph message-passing neural network with a densely self-connected mechanism results in robust performance on benchmark datasets.
- The approach outperforms several baseline methods and shows effectiveness on synthetic glyphs and real-world building footprints.
- PolyMP and PolyMP-DSC effectively capture expressive geometric features that remain invariant under common transformations like translation, rotation, scaling, and shearing, while also being robust to trivial vertex removals.
- The proposed approach exhibits strong generalization ability, allowing the transfer of learned geometric features from synthetic glyphs to real-world building footprints.
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