PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images

• URL: http://arxiv.org/abs/2111.15491v1
• Date: Tue, 30 Nov 2021 15:23:17 GMT
• Title: PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images
• Authors: Stefano Zorzi, Shabab Bazrafkan, Stefan Habenschuss, Friedrich Fraundorfer
• Abstract summary: This paper introduces PolyWorld, a neural network that directly extracts building vertices from an image and connects them correctly to create precise polygons. PolyWorld significantly outperforms the state-of-the-art in building polygonization.
• Score: 10.661430927191205
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Most state-of-the-art instance segmentation methods produce binary segmentation masks, however, geographic and cartographic applications typically require precise vector polygons of extracted objects instead of rasterized output. This paper introduces PolyWorld, a neural network that directly extracts building vertices from an image and connects them correctly to create precise polygons. The model predicts the connection strength between each pair of vertices using a graph neural network and estimates the assignments by solving a differentiable optimal transport problem. Moreover, the vertex positions are optimized by minimizing a combined segmentation and polygonal angle difference loss. PolyWorld significantly outperforms the state-of-the-art in building polygonization and achieves not only notable quantitative results, but also produces visually pleasing building polygons. Code and trained weights will be soon available on github.

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