Road Segmentation
30 papers with code • 3 benchmarks • 5 datasets
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.
Benchmarks
These leaderboards are used to track progress in Road Segmentation
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Datasets
DeepGlobe
Massachusetts Roads Dataset
Pothole Mix
Polarimetric Imaging for Perception
ChesapeakeRSC
Most implemented papers
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
While most approaches to semantic reasoning have focused on improving performance, in this paper we argue that computational times are very important in order to enable real time applications such as autonomous driving.
D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction
twigsWHy/dlinknet
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CVPR 2018 2018
Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the past decade.
Deep Quaternion Networks
The field of deep learning has seen significant advancement in recent years.
Road Segmentation Using CNN and Distributed LSTM
Evvvvvvvva/AutonomousDriving
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In automated driving systems (ADS) and advanced driver-assistance systems (ADAS), an efficient road segmentation is necessary to perceive the drivable region and build an occupancy map for path planning.
SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments
We present a novel approach for unsupervised road segmentation in adverse weather conditions such as rain or fog.
RoadTracer: Automatic Extraction of Road Networks from Aerial Images
mitroadmaps/roadtracer
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CVPR 2018
Mapping road networks is currently both expensive and labor-intensive.
Semantic Binary Segmentation using Convolutional Networks without Decoders
In this paper, we propose an efficient architecture for semantic image segmentation using the depth-to-space (D2S) operation.
NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local Operations
Road extraction from very high resolution satellite (VHR) images is one of the most important topics in the field of remote sensing.
Spatial Information Inference Net: Road Extraction Using Road-Specific Contextual Information
ErenTuring/SIINet
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ISPRS Journal of Photogrammetry and Remote Sensing 2019
The validation experiments using three large datasets of very high-resolution (VHR) satellite imagery show that the proposed method can improve road extraction accuracy and provide an output that is more in line with human expectations.
Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks
tom-roddick/mono-semantic-maps
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CVPR 2020
Autonomous vehicles commonly rely on highly detailed birds-eye-view maps of their environment, which capture both static elements of the scene such as road layout as well as dynamic elements such as other cars and pedestrians.
