Research map / Detection and segmentation / CNN features
Improved Road Connectivity by Joint Learning of Orientation and Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| jointly learn road orientation and segmentation (ablation) | road extraction models · SpaceNet | APLS: 55.69 -> 60.77 | Helped |
| jointly learn road orientation and segmentation (ablation) | road extraction models · DeepGlobe | APLS: 65.33 -> 68.71 | Helped |
| use junction prediction instead of orientation learning (ablation) | ResNet18 road extraction · SpaceNet | APLS: 52.65 -> 52.76 | No clear effect |
| use two multi-branch stacks rather than one (ablation) | orientation-and-segmentation model · SpaceNet | APLS: 60.92 -> 63.65 | Helped |
| increase the multi-branch model from two stacks to three (ablation) | orientation-and-segmentation model · SpaceNet | APLS: 63.65 -> 62.89 | Hurt |
| use two multi-branch stacks rather than one (ablation) | orientation-and-segmentation model · DeepGlobe | APLS: 70.23 -> 73.21 | Helped |
| increase the multi-branch model from two stacks to three (ablation) | orientation-and-segmentation model · DeepGlobe | APLS: 73.21 -> 72.48 | Hurt |
| add summed cross-task feature fusion (ablation) | two-stack multi-branch model · SpaceNet | APLS: 61.78 -> 63.65 | Helped |
| add connectivity refinement after segmentation (ablation) | full road extraction pipeline · SpaceNet and DeepGlobe | APLS | Helped |
Closest papers by meaning
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- LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving (ICLR 2024 · similarity 0.84)
- CVGL: Causal Learning and Geometric Topology (NeurIPS 2025 · similarity 0.84)
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