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 61–70 of 82 papers

TitleStatusHype
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
PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise BinarizationCode0
Semantic Binary Segmentation using Convolutional Networks without DecodersCode0
D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road ExtractionCode0
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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