Yamaha-CMU Off-Road Dataset (YCOR)
The Yamaha-CMU Off-Road (YCOR) dataset is a semantic segmentation benchmark for autonomous off-road navigation, released by CMU AirLab. It contains 1,076 egocentric RGB images collected from a Yamaha off-road vehicle at four locations in Western Pennsylvania and Ohio, spanning three different seasons to capture varied terrain and lighting conditions. Each image is densely pixel-labeled into 8 classes (sky, rough trail, smooth trail, traversable grass, high vegetation, non-traversable low vegetation, obstacle, and puddle) to support terrain traversability estimation. Labels were created with a polygon-based annotation interface, densified via Dense CRF post-processing, then manually inspected and corrected. The dataset is split into 931 training and 145 validation images, with the split generated so that no data-collection session overlaps between train and validation. It accompanies the 2018 Field and Service Robotics paper 'Real-Time Semantic Mapping for Autonomous Off-Road Navigation' by Maturana, Chou, Uenoyama, and Scherer, and is widely used for off-road semantic segmentation and traversability research.
- Scale
- Not declared
- Formats
- custom
- License
- CC-BY-4.0
- Published
- 2018-05-01
Decision summary
passive_log
Metadata requires review against the official source before publication.
Read the dataset manifest and feature schema.
Catalog assessment
Selection evidence
75/100
Provisional · confidence 26 · 1/6 evaluated
Access and governance
Access and license are declared by the source.
Schema and signal coverage
No machine-readable schema has been verified yet.
Policy training readiness
Observation/action alignment has not been established.
View 3 more dimensions
World-model readiness
World-model observation, geometry, or temporal semantics are not verified.
Failure and recovery readiness
No verified failure/recovery annotation evidence is available yet.
Download and processing readiness
Dataset scale and sampling path are unknown.
Review unresolved evidence and next checks
Signal gaps
- Action / hand pose / robot state. Not enough evidence is available to classify this signal.
- Gaze / attention. Not enough evidence is available to classify this signal.
- Feedback / correction / failure. Not enough evidence is available to classify this signal.
- Sim-real pairing. Not enough evidence is available to classify this signal.
Next checks
- Read the dataset manifest and feature schema.
- Run a bounded sample audit before assigning Strong readiness.
Metadata coverage
Loop signals
3/7 present or partial
No decision-grade evidence captured yet.
unknownNo decision-grade evidence captured yet.
unknownNo decision-grade evidence captured yet.
unknownView 4 more signal categories
video · The Yamaha-CMU Off-Road (YCOR) dataset is a semantic segmentation benchmark for autonomous off-road navigation, released by CMU AirLab. It contains 1,076 egocentric RGB images collected from a Yamaha off-road vehicle at four locations in Western Pennsylvania and Ohio, spanning three different seasons to capture varied terrain and lighting conditions. Each image is densely pixel-labeled into 8 classes (sky, rough trail, smooth trail, traversable grass, high vegetation, non-traversable low vegetation, obstacle, and puddle) to support terrain traversability estimation. Labels were created with a polygon-based annotation interface, densified via Dense CRF post-processing, then manually inspected and corrected. The dataset is split into 931 training and 145 validation images, with the split generated so that no data-collection session overlaps between train and validation. It accompanies the 2018 Field and Service Robotics paper 'Real-Time Semantic Mapping for Autonomous Off-Road Navigation' by Maturana, Chou, Uenoyama, and Scherer, and is widely used for off-road semantic segmentation and traversability research.
presentThe Yamaha-CMU Off-Road (YCOR) dataset is a semantic segmentation benchmark for autonomous off-road navigation, released by CMU AirLab. It contains 1,076 egocentric RGB images collected from a Yamaha off-road vehicle at four locations in Western Pennsylvania and Ohio, spanning three different seasons to capture varied terrain and lighting conditions. Each image is densely pixel-labeled into 8 classes (sky, rough trail, smooth trail, traversable grass, high vegetation, non-traversable low vegetation, obstacle, and puddle) to support terrain traversability estimation. Labels were created with a polygon-based annotation interface, densified via Dense CRF post-processing, then manually inspected and corrected. The dataset is split into 931 training and 145 validation images, with the split generated so that no data-collection session overlaps between train and validation. It accompanies the 2018 Field and Service Robotics paper 'Real-Time Semantic Mapping for Autonomous Off-Road Navigation' by Maturana, Chou, Uenoyama, and Scherer, and is widely used for off-road semantic segmentation and traversability research.
partialNo decision-grade evidence captured yet.
unknownOpen · CC-BY-4.0 · custom
partialEvidence details and provenance
Official signal claims
Schema and annotations
No machine-readable schema facts are captured.
Sample verification
Pending. Metadata does not prove sample coverage, alignment, or file integrity.
curated official source
Integration notes
- Metadata requires review against the official source before publication.
Catalog links