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Holistically-Nested Structure-Aware Graph Neural Network for Road Extraction

arXiv 2407.02639 ↗First posted 2 Jul 2024cs.CVcited by 0

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Abstract (arXiv)

Convolutional neural networks (CNN) have made significant advances in detecting roads from satellite images. However, existing CNN approaches are generally repurposed semantic segmentation architectures and suffer from the poor delineation of long and curved regions. Lack of overall road topology and structure information further deteriorates their performance on challenging remote sensing images. This paper presents a novel multi-task graph neural network (GNN) which simultaneously detects both road regions and road borders; the inter-play between these two tasks unlocks superior performance from two perspectives: (1) the hierarchically detected road borders enable the network to capture and encode holistic road structure to enhance road connectivity (2) identifying the intrinsic correlation of semantic landcover regions mitigates the difficulty in recognizing roads cluttered by regions with similar appearance. Experiments on challenging dataset demonstrate that the proposed architecture can improve the road border delineation and road extraction accuracy compared with the existing methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 71 references that are in the map. First posted on arXiv 2 Jul 2024.

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19 of 71 references.

References · 19

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7 more
  1. 2021Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Trackingcited by 37
  2. 2021GraphFPN: Graph Feature Pyramid Network for Object Detectioncited by 25
  3. 2020Promoting Connectivity of Network-Like Structures by Enforcing Region Separationcited by 19
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  6. 2020Spectral Pyramid Graph Attention Network for Hyperspectral Image Classificationcited by 7
  7. 2016Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentationcited by 6

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