WIT-UAS (Wildland-fire Infrared Thermal UAS Dataset)
WIT-UAS is a wildland-fire long-wave infrared (LWIR) thermal dataset collected by CMU's AirLab (Airborne Robotics Lab) to detect crew and vehicle assets from aerial views amidst prescribed burns.
Source notes
WIT-UAS is a wildland-fire long-wave infrared (LWIR) thermal dataset collected by CMU's AirLab (Airborne Robotics Lab) to detect crew and vehicle assets from aerial views amidst prescribed burns. It is split into two subsets: WIT-UAS-ROS (full ROS bag files containing sensor and robot data of UAS flights over fire) and WIT-UAS-Image (hand-labeled LWIR images extracted from the bags at ~1 frame per second). The dataset contains 6,951 total thermal images, of which 2,062 are manually bounding-box annotated, yielding 5,030 labeled vehicles and 1,542 labeled humans. The dataset.classes file defines the categories: person, car, bicycle, othervehicle, plus noobject and dontcare. Data was collected over three prescribed fire seasons (fall 2021, spring 2022, fall 2022) at Pennsylvania State Game Lands (SGL 174 near Rossiter, SGL 111 near Confluence, SGL 42 in Reade Township, all in Western PA) using DJI M100 and DJI M600 UAVs, with thermal sensors connected to an onboard NVIDIA Jetson Xavier NX. The dataset addresses the problem that thermal detectors trained without fire data frequently misclassify flames as people; it is the first public LWIR dataset focused on assets near fire. Code, pretrained YOLO/SSD models, and download scripts (via minio) are released on GitHub under GPL-3.0. Published at IEEE/RSJ IROS 2023.
- Scale
- Not declared
- Formats
- custom · rosbag
- License
- custom
- Published
- 2023-12-14
Decision summary
passive_log
Metadata requires review against the official source before publication.
Read the dataset manifest and feature schema.
Release history
Source-backed release timing for this canonical dataset record.
- Release evidence
Dataset series published
Release timing is recorded from official dataset metadata.
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
75/100
Provisional
1/6 dimensions scored · 26% confidence
Read the evidence behind all 6 dimensions
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.
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
- 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.
Loop signals
4/7 present or partial
No decision-grade evidence captured yet.
unknownNo decision-grade evidence captured yet.
unknownView 5 more signal categories
WIT-UAS is a wildland-fire long-wave infrared (LWIR) thermal dataset collected by CMU's AirLab (Airborne Robotics Lab) to detect crew and vehicle assets from aerial views amidst prescribed burns. It is split into two subsets: WIT-UAS-ROS (full ROS bag files containing sensor and robot data of UAS flights over fire) and WIT-UAS-Image (hand-labeled LWIR images extracted from the bags at ~1 frame per second). The dataset contains 6,951 total thermal images, of which 2,062 are manually bounding-box annotated, yielding 5,030 labeled vehicles and 1,542 labeled humans. The dataset.classes file defines the categories: person, car, bicycle, othervehicle, plus noobject and dontcare. Data was collected over three prescribed fire seasons (fall 2021, spring 2022, fall 2022) at Pennsylvania State Game Lands (SGL 174 near Rossiter, SGL 111 near Confluence, SGL 42 in Reade Township, all in Western PA) using DJI M100 and DJI M600 UAVs, with thermal sensors connected to an onboard NVIDIA Jetson Xavier NX. The dataset addresses the problem that thermal detectors trained without fire data frequently misclassify flames as people; it is the first public LWIR dataset focused on assets near fire. Code, pretrained YOLO/SSD models, and download scripts (via minio) are released on GitHub under GPL-3.0. Published at IEEE/RSJ IROS 2023.
partialWIT-UAS is a wildland-fire long-wave infrared (LWIR) thermal dataset collected by CMU's AirLab (Airborne Robotics Lab) to detect crew and vehicle assets from aerial views amidst prescribed burns. It is split into two subsets: WIT-UAS-ROS (full ROS bag files containing sensor and robot data of UAS flights over fire) and WIT-UAS-Image (hand-labeled LWIR images extracted from the bags at ~1 frame per second). The dataset contains 6,951 total thermal images, of which 2,062 are manually bounding-box annotated, yielding 5,030 labeled vehicles and 1,542 labeled humans. The dataset.classes file defines the categories: person, car, bicycle, othervehicle, plus noobject and dontcare. Data was collected over three prescribed fire seasons (fall 2021, spring 2022, fall 2022) at Pennsylvania State Game Lands (SGL 174 near Rossiter, SGL 111 near Confluence, SGL 42 in Reade Township, all in Western PA) using DJI M100 and DJI M600 UAVs, with thermal sensors connected to an onboard NVIDIA Jetson Xavier NX. The dataset addresses the problem that thermal detectors trained without fire data frequently misclassify flames as people; it is the first public LWIR dataset focused on assets near fire. Code, pretrained YOLO/SSD models, and download scripts (via minio) are released on GitHub under GPL-3.0. Published at IEEE/RSJ IROS 2023.
partialWIT-UAS is a wildland-fire long-wave infrared (LWIR) thermal dataset collected by CMU's AirLab (Airborne Robotics Lab) to detect crew and vehicle assets from aerial views amidst prescribed burns. It is split into two subsets: WIT-UAS-ROS (full ROS bag files containing sensor and robot data of UAS flights over fire) and WIT-UAS-Image (hand-labeled LWIR images extracted from the bags at ~1 frame per second). The dataset contains 6,951 total thermal images, of which 2,062 are manually bounding-box annotated, yielding 5,030 labeled vehicles and 1,542 labeled humans. The dataset.classes file defines the categories: person, car, bicycle, othervehicle, plus noobject and dontcare. Data was collected over three prescribed fire seasons (fall 2021, spring 2022, fall 2022) at Pennsylvania State Game Lands (SGL 174 near Rossiter, SGL 111 near Confluence, SGL 42 in Reade Township, all in Western PA) using DJI M100 and DJI M600 UAVs, with thermal sensors connected to an onboard NVIDIA Jetson Xavier NX. The dataset addresses the problem that thermal detectors trained without fire data frequently misclassify flames as people; it is the first public LWIR dataset focused on assets near fire. Code, pretrained YOLO/SSD models, and download scripts (via minio) are released on GitHub under GPL-3.0. Published at IEEE/RSJ IROS 2023.
partialNo decision-grade evidence captured yet.
unknownOpen · custom · custom · rosbag
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.