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Joint Semantic-geometric Learning for Polygonal Building Segmentation

AAAI 2021 · Detection and segmentation › CNN features · approach: Context modeling for semantic segmentation · 35 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
jointly predict building segmentation, corner classes, and edge orientations, then generate and refine polygon verticesRes-U-Net polygonal building segmentation · CrowdAIF1: 71.7 -> 73.2Helped
jointly predict building segmentation, corner classes, and edge orientations, then generate and refine polygon verticesRes-U-Net polygonal building segmentation · VegasF1-score: 88.5 -> 89.4Helped
use the proposed polygonal prediction method instead of ASIPCrowdAI · CrowdAIvertex F1₃px: 60.32 -> 66.96Helped
use the proposed polygonal prediction method instead of ASIPCrowdAI · CrowdAIvertex F1₅px: 78.00 -> 84.82Helped
add the vertex generation module to replace Douglas-Peucker simplification (ablation)multi-task segmentation baseline · Vegasvertex F1₃px: 50.94 -> 54.54Helped
add the vertex generation module to replace Douglas-Peucker simplification (ablation)multi-task segmentation baseline · Vegasvertex F1₅px: 75.47 -> 78.25Helped
add polygon refinement network to VGM-generated vertices (ablation)VGM polygon prediction · Vegasvertex F1₃px: 54.54 -> 65.37Helped
add polygon refinement network to VGM-generated vertices (ablation)VGM polygon prediction · Vegasvertex F1₅px: 78.25 -> 81.24Helped

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