OpenBot
Platform
Egocentric datasetOpenReadiness 75/100Provisional

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…

Source notes

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
Official sourceAll recordsDataset index A–ZRun local preflightRequest scoped audit

Decision summary

Best for

passive_log

Main blocker

Metadata requires review against the official source before publication.

Next check

Read the dataset manifest and feature schema.

Release history

Source-backed release timing for this canonical dataset record.

Important releases

  1. Dataset series published

    Release timing is recorded from official dataset metadata.

    Release evidence

Selection readiness

6 evidence dimensions for deciding whether this dataset is ready to inspect, compare, or adopt. This is not a model benchmark.

Catalog evidence · not task performance

How scores work

75/100

Provisional

1/6 dimensions scored · 26% confidence

Access and governance75conf. 85
Schema and signal coverageNot scoredconf. 15
Policy training readinessNot scoredconf. 15
World-model readinessNot scoredconf. 15
Failure and recovery readinessNot scoredconf. 15
Download and processing readinessNot scoredconf. 10
Read the evidence behind all 6 dimensions

Access and governance

Access and license are declared by the source.

usefulfit 75 · confidence 85

Schema and signal coverage

No machine-readable schema has been verified yet.

unknownnot scored · confidence 15

Policy training readiness

Observation/action alignment has not been established.

unknownnot scored · confidence 15

World-model readiness

World-model observation, geometry, or temporal semantics are not verified.

unknownnot scored · confidence 15

Failure and recovery readiness

No verified failure/recovery annotation evidence is available yet.

unknownnot scored · confidence 15

Download and processing readiness

Dataset scale and sampling path are unknown.

unknownnot scored · confidence 10
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.
Engineering checks (OBRS) behind this record
Engineering checksBronze

Needs Audit

1/12checks passed

OBRS metadata and reported test evidence. A breakdown behind Selection readiness, not a separate score or an independent OpenBot certification.

Standardization & Loaders2 / 25 pt
Physical & Action Quality2 / 25 pt
Semantic & Annotation4 / 20 pt
Real-World Validation0 / 15 pt
License & Compliance10 / 15 pt
Missing evidence and readiness gaps
  • A passing real-hardware test report is required.
  • A passing ingestion/pipeline test report is required.
  • A passing privacy and provenance review is required.

Dataset facts

Source
CMU AirLab
Evidence
secondary claim
Formats
custom
Read paper

Loop signals

3/7 present or partial

Action / hand pose / robot state

No decision-grade evidence captured yet.

unknown
Gaze / attention

No decision-grade evidence captured yet.

unknown
View 5 more signal categories
Observation / ego video

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.

present
Language intent / task phase

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.

partial
Feedback / correction / failure

No decision-grade evidence captured yet.

unknown
Sim-real pairing

No decision-grade evidence captured yet.

unknown
License / format / access

Open · CC-BY-4.0 · custom

partial
Evidence details and provenance

Official signal claims

SegmentationVideo

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.