SOTAVerified

Road Segmentation

Road Segmentation is a pixel wise binary classification in order to extract underlying road network. Various Heuristic and data driven models are proposed. Continuity and robustness still remains one of the major challenges in the area.

Papers

Showing 41–50 of 82 papers

TitleStatusHype
Road Segmentation Using CNN with GRU—0
Self-Supervised Relative Depth Learning for Urban Scene Understanding—0
Solving Learn-to-Race Autonomous Racing Challenge by Planning in Latent Space—0
Technical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Boosting Off-Road Segmentation via Photometric Distortion and Exponential Moving Average—0
TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation—0
UdeerLID+: Integrating LiDAR, Image, and Relative Depth with Semi-Supervised—0
VecRoad: Point-Based Iterative Graph Exploration for Road Graphs Extraction—0
Visual Traffic Knowledge Graph Generation from Scene Images—0
PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation—0
Accurate Urban Road Centerline Extraction from VHR Imagery via Multiscale Segmentation and Tensor Voting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1U-Net (ResNet-18)DWR46.5—Unverified
2DeepLabV3+ (ResNet-18)DWR46.1—Unverified
3U-Net (ResNet-50)DWR45.7—Unverified
4FCNDWR10.7—Unverified
#ModelMetricClaimedVerifiedStatus
1CoANet + PRNmIoU70.6—Unverified
2SPIN Road Mapper (ours)APLS0.74—Unverified
3D-LinkNetIoU0.64—Unverified
#ModelMetricClaimedVerifiedStatus
1RSM-SSIoU67.35—Unverified
2SPIN Road Mapper (ours)IoU65.24—Unverified