---
license: cc-by-4.0
language:
  - en
pretty_name: "DRINF: Dhaka Road Intersection Nadir Footage"
task_categories:
  - object-detection
  - video-classification
tags:
  - drone
  - uav
  - aerial-imagery
  - traffic
  - transportation
  - intelligent-transportation-systems
  - computer-vision
  - vehicle-tracking
  - trajectory-prediction
  - surrogate-safety
  - road-intersections
  - nadir
  - dhaka
  - bangladesh
---

# DRINF: Dhaka Road Intersection Nadir Footage

## Overview

**DRINF (Dhaka Road Intersection Nadir Footage)** is a large-scale drone-based traffic dataset containing nadir (top-down) videos of road intersections across **Dhaka, Bangladesh**.

The dataset is intended to support research in:

- Vehicle detection
- Multi-object tracking
- Traffic flow analysis
- Vehicle trajectory extraction
- Trajectory prediction
- Surrogate safety analysis
- Intelligent Transportation Systems (ITS)
- Computer vision
- Machine learning
- Autonomous driving research
- Urban traffic analytics

The videos are captured using a DJI drone under real-world traffic conditions.

---

# Dataset Contents

The dataset currently contains:

- Drone videos
- Nadir-view footage
- Multiple road intersections
- Different traffic densities
- Different weather and lighting conditions (where available)

Future releases may include:

- Vehicle trajectories
- Detection annotations
- Tracking annotations
- Camera calibration
- Metadata
- Traffic statistics

---

# Directory Structure

Example:

```
DRINF/
│
├── Nadirs/
│   ├── Polashi Bazaar/
│   ├── Science Lab/
│   ├── Nilkhet/
│   └── ...
│
├── README.md
└── LICENSE
```

---

# Data Collection

Platform:

- DJI Mini 2

Viewpoint:

- Nadir (Top-down)

Environment:

- Urban road intersections
- Mixed traffic
- Real traffic conditions

Country:

- Bangladesh

City:

- Dhaka

---

# Potential Research Applications

This dataset can be used for:

- Vehicle detection
- Vehicle counting
- Traffic density estimation
- Multi-object tracking
- Vehicle speed estimation
- Vehicle trajectory prediction
- Traffic simulation
- Traffic conflict analysis
- Surrogate Safety Measures (SSM)
- Accident risk estimation
- Deep learning
- Vision-Language Models
- Large Multimodal Models (LMMs)
- Geographic information systems
- Transportation engineering

---

# License

This dataset is distributed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.

You are free to:

- Share
- Copy
- Redistribute
- Adapt
- Remix
- Transform
- Build upon the material

for any purpose, including commercial use,

provided that appropriate attribution is given.

License:

https://creativecommons.org/licenses/by/4.0/

---

# Citation

If you use this dataset in your research, publications, reports, or software, please cite the dataset.

## Dataset Citation

```bibtex
@dataset{mahmud_drinf,
  author       = {Hamim Mahmud},
  title        = {DRINF: Dhaka Road Intersection Nadir Footage},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/hamimmahmud0/DTINF},
  license      = {CC BY 4.0}
}
```

Additionally, please cite any accompanying publication if one becomes available.

---

# Citation Requirements

Users of this dataset must:

1. Cite the dataset in any academic publication.
2. Include the dataset name:
   **DRINF: Dhaka Road Intersection Nadir Footage**.
3. Provide the dataset URL whenever possible.
4. State that the dataset is licensed under **CC BY 4.0**.

---

# Ethical Considerations

- Videos were collected in public road environments.
- The dataset is intended solely for research, education, and scientific purposes.
- Users are responsible for complying with local laws and institutional ethics policies.
- Dataset maintainers are not responsible for misuse of the data.

---

# Version

Current Version:

**v1.0**

Future versions may expand with:

- More intersections
- More cities
- Longer recordings
- Vehicle annotations
- Tracking labels
- Trajectory datasets
- Calibration parameters

---

# Contact

For questions, suggestions, or collaborations, please open an issue on the dataset repository or contact the dataset maintainer through the Hugging Face profile.

---

# Acknowledgements

Thanks to everyone who contributed to the collection, organization, and validation of the DRINF dataset.

We hope this dataset supports advances in transportation engineering, intelligent transportation systems, and computer vision research.