---
license: cc-by-nc-4.0
task_categories:
  - object-detection
  - visual-question-answering
  - image-segmentation
tags:
  - autonomous-driving
  - indian-roads
  - scene-understanding
  - multimodal
size_categories:
  - 1K<n<10K
---

# Roadscapes Dataset

A multitask multimodal dataset for autonomous driving scene understanding in diverse Indian road environments.

## Overview

Roadscapes is a comprehensive dataset designed to advance research in visual scene understanding for autonomous driving systems, with a particular focus on unstructured and diverse driving environments typical of Indian roads. The dataset consists of 8,981 annotated images capturing various road scenarios, accompanied by question-answer pairs for multiple vision-language tasks.

## Key Features

- 8,981 images from diverse Indian driving environments
- Manually verified bounding boxes for object detection in LabelMe JSON format
- Segmentation masks
- Scene attributes derived through rule-based heuristics
- Question-answer pairs for object grounding, visual reasoning, and scene understanding
- Diverse environments: urban, rural, highways, village paths, congested city streets
- Daytime and nighttime captures
- All images anonymized — faces and license plates blurred, no GPS/geotag data

## Dataset Statistics

| Split | Daytime | Nighttime | Total |
|-------|---------|-----------|-------|
| Train | 5,519   | 1,989     | 7,508 |
| Test  | 1,277   | 196       | 1,473 |
| **Total** | **6,796** | **2,185** | **8,981** |

- Train/Test split: ~84% / 16%

## Dataset Structure
```
roadscapes_data/
├── image_data/
│   ├── test/
│   │   ├── Sequence_Day_n/
│   │   │   ├── image.jpg
│   │   │   └── image.json      ← LabelMe format
│   │   └── Sequence_Night_n/
│   └── train/
│       ├── Sequence_Day_n/
│       │   ├── image.jpg
│       │   └── image.json      ← LabelMe format
│       └── Sequence_Night_n/
├── segmentation_masks/
│   ├── test/
│   └── train/
├── vqa_train.csv               ← QA annotations
├── vqa_test.csv                ← QA annotations
└── README.md
```

## Annotation Format

**Object Detection** — Each image has a paired JSON file in [LabelMe](https://github.com/labelmeai/labelme) format:
```json
{
  "version": "5.0.0",
  "flags": {},
  "shapes": [
    {
      "label": "car",
      "points": [[x1, y1], [x2, y2]],
      "shape_type": "rectangle",
      "flags": {}
    }
  ],
  "imagePath": "image.jpg",
  "imageHeight": 720,
  "imageWidth": 1280
}
```

**VQA Annotations** — Stored as CSV files with the following structure:

| Column | Description |
|--------|-------------|
| image_id | Reference to the corresponding image |
| question | Natural language question about the scene |
| answer | Ground truth answer |
| task | Task type (object_counting / object_description / surrounding_description) |

**Segmentation Masks** — Stored as images in the `segmentation_masks/` directory, aligned with the corresponding images in `image_data/`.

## Tasks Supported

1. **Object Grounding**: Locating and identifying objects in road scenes
2. **Visual Reasoning**: Understanding relationships and interactions between scene elements
3. **Scene Understanding**: Comprehensive interpretation of driving environments

## Capture Setup

Images were captured using a monocular camera across various Indian locations to ensure geographic and environmental diversity, covering different times of day, weather conditions, and road types.

## Baseline Model Performance (Accuracy)

| Model | Object Counting | Object Description | Surrounding Description |
|-------|----------------|-------------------|------------------------|
| GPT-4o | 0.598 | 0.495 | 0.701 |
| Paligemma | 0.187 | 0.501 | 0.485 |
| Phi-3.5 | **0.667** | 0.437 | 0.643 |
| GPT-4o-mini | 0.628 | 0.453 | 0.645 |

## Privacy & Ethics

- All faces blurred
- All license plates blurred
- No GPS or geotag data included
- No images near sensitive or restricted locations

## Citation

If you use Roadscapes in your research, please cite our paper:
```bibtex
@article{iyer2026roadscapesqa,
  title={RoadscapesQA: A Multitask, Multimodal Dataset for Visual Question Answering on Indian Roads},
  author={Iyer, Vijayasri and Rathinagiriswaran, Maahin and S, Jyothikamalesh},
  journal={arXiv preprint arXiv:2602.12877},
  year={2026}
}
```

## License

The dataset is released for non-commercial use only under **CC BY-NC 4.0**.

## Contact

For questions, issues, or collaboration: thisisvij98@gmail.com

## Acknowledgments

We thank all contributors involved in data collection, annotation, and verification. This work aims to advance autonomous driving research for challenging and unstructured environments.