README.md

---
license: cc-by-nc-4.0
task_categories:
  - image-segmentation
tags:
  - robotics
  - traversability
  - semantic-segmentation
  - construction
  - lidar
---
# Construction Traversability Dataset

A construction-site RGB semantic segmentation dataset developed for research on
terrain understanding, traversability estimation, and multimodal RGB–LiDAR
perception for mobile robots.

## Overview

This dataset contains RGB images and pixel-wise semantic segmentation masks
collected in construction-site environments. The dataset is intended to support
research on construction-site scene understanding and traversability-aware
robot perception.

The release also includes the camera/LiDAR calibration used for RGB–LiDAR
projection and the fine-tuned semantic segmentation model used in the associated
research.

## Dataset Contents

```text
construction-traversability-dataset/
├── README.md
├── LICENSE
├── dataset.yaml
│
├── images/
│   ├── train/
│   └── val/
│
├── masks/
│   ├── train/
│   └── val/
│
├── calibration/
│   ├── camera_intrinsics.yaml
│   └── lidar_camera_extrinsics.yaml
│
├── model/
│   └── model_config.yaml
│   └── yolo26_sem_construction.pt
```

### RGB Images

RGB images are provided under:

- `images/train/`
- `images/val/`

### Semantic Masks

Each RGB image has a corresponding pixel-wise semantic segmentation mask
under the `masks/` directory.

The mask uses integer class IDs, with the class definitions given below.
The RGB image and mask use corresponding filenames.

This is a pixel-wise semantic segmentation representation rather than COCO
polygon/RLE annotation format.

## Semantic Classes

The dataset uses the following 28-class construction-site taxonomy:

| ID | Class |
|---:|---|
| 0 | animal |
| 1 | building |
| 2 | ceiling |
| 3 | concrete_blocks |
| 4 | construction_machinery |
| 5 | debris |
| 6 | fence |
| 7 | flat_road |
| 8 | material_pile |
| 9 | not_visible |
| 10 | person |
| 11 | pillar |
| 12 | pipes |
| 13 | pit |
| 14 | pole |
| 15 | ponding_concrete |
| 16 | puddle |
| 17 | rebar |
| 18 | rocky_terrain |
| 19 | scaffolding |
| 20 | sky |
| 21 | terrain |
| 22 | tiles |
| 23 | vegetation |
| 24 | vehicle |
| 25 | wall |
| 26 | wet_mud |
| 27 | wooden_planks |

The class IDs correspond to the dataset taxonomy used by the semantic
segmentation and RGB–LiDAR fusion pipeline.

## Dataset Configuration

`dataset.yaml` contains the train/validation paths and class names used by the
training pipeline.

Example:

```yaml
path: .
train: images/train
val: images/val
names:
  0: animal
  1: building
  ...
```

The repository's `dataset.yaml` should be treated as the authoritative training
configuration.

## Camera and LiDAR Calibration

The `calibration/` directory contains the sensor parameters used by the
RGB–LiDAR projection pipeline.

### Camera Intrinsics

`calibration/camera_intrinsics.yaml` contains the OAK-D RGB camera intrinsic
matrix used for projection.

The projection code uses a 640 × 480 image size and:

```text
fx = 513.8645629882812
fy = 513.7389526367188
cx = 316.9952392578125
cy = 247.48223876953125
```

The supplied calibration file does not specify distortion coefficients.

### LiDAR–Camera Extrinsics

`calibration/lidar_camera_extrinsics.yaml` contains the transforms used to
relate the Livox LiDAR and OAK-D RGB camera frames through the
`base_footprint` frame, including the optical-frame correction used by the
fusion pipeline.

These parameters document the transforms used by the released processing
pipeline. They should not be interpreted as an independently certified
metrology calibration unless otherwise stated.

## Fine-Tuned Semantic Segmentation Model

The `model/` directory contains the fine-tuned semantic segmentation model
used for the construction-site taxonomy.

The model is based on the YOLO26 semantic segmentation architecture and is
fine-tuned for the 28 construction-site classes listed above.

The model is provided to facilitate reproducibility of the semantic
segmentation and RGB–LiDAR fusion experiments.

## Raw ROS 2 Data

The complete ROS 2 recordings used during data collection may be released
separately because of their large file size.

**Raw ROS bag repository:**  
https://huggingface.co/datasets/manojkarnekar/construction-rosbags
The raw recordings are intended to provide the original sensor data and
timestamps needed for reproduction and further research.

## Data Format

The released annotated data is organized as paired RGB images and
pixel-wise semantic masks.

This format is intentionally kept simple so that it can be used directly by
semantic segmentation pipelines without requiring conversion from COCO
polygon/RLE annotations.

Researchers who require another annotation format may convert the masks to
their preferred representation.

## Intended Use

This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. Commercial use is not permitted. See LICENSE for the full license terms.

Possible applications include:

- construction-site semantic segmentation
- terrain and traversability perception
- RGB–LiDAR fusion
- mobile robot navigation
- construction-site scene understanding
- semantic costmap generation
- multimodal robotic perception

## License

The dataset and associated materials in this repository are released under
the **Creative Commons Attribution-NonCommercial 4.0 International
(CC BY-NC 4.0)** license.

Commercial use is not permitted under this license.

See [`LICENSE`](LICENSE) for the full license text.

License information:
https://creativecommons.org/licenses/by-nc/4.0/

## Disclaimer

The dataset is provided for research purposes. No guarantee is made regarding
the completeness, accuracy, or suitability of the data for a particular
application. Users are responsible for validating the data before deploying
models or systems based on it in real-world environments.

## Contact

manojkarnekar1@gmail.com