Simuletic/CCTV-Smoke-Fire-Emergency-Detection-Dataset

CCTV Smoke & Fire Emergency Detection Dataset

Early-stage fire detection dataset featuring small ignition points, bin fires, and smoldering debris from a surveillance perspective.

---

🧐 Overview

CCTV Smoke & Fire is a specialized open-source synthetic dataset for Computer Vision (CV) tasks focused on Emergency Response, Smart City Safety, and Incident Monitoring.

The most critical fires are the ones detected in their first 60 seconds. While most fire datasets focus on large-scale forest fires or fully developed structural conflagrations, this dataset is specifically designed to train models to identify early-stage ignition: a smoking trash bin, a small paper fire on a sidewalk, or a discarded cigarette smoldering in the grass—all captured from the realistic, high-angle perspective of a Public Video Surveillance (CCTV) camera.

🚀 Need more data?

This 220-image set is a sample from the Simuletic Emergency Series. We provide hyper-realistic synthetic data to solve the most difficult detection challenges in public safety and infrastructure monitoring.

• Full Smoke & Fire Dataset: Over 2,000+ images covering 50+ diverse urban and industrial scenarios.
• Custom Sequences: High-fidelity video sequences available for temporal/optical flow fire analysis. Explore the full library at: simuletic.com/datasets

---

✨ Key Features

• Early Detection Focus: Specifically targets "starting fires" (small flames, thin wisps of smoke, smoldering) rather than fully developed fires.
• Surveillance Perspective: Captured from 10–20 foot mounting heights with realistic CCTV distortion, compression artifacts, and low-light noise.
• High-Risk Scenarios: Focused on urban "micro-incidents" including trash bin ignitions, localized paper fires, and dry grass/vegetation smoldering.
• Privacy-First: 100% synthetic. No real public areas, PII, or real-world incidents are depicted, ensuring full GDPR compliance for AI R&D.

---

📊 Dataset Structure

The dataset consists of synthetic image files and a central metadata.jsonl file containing ground truth descriptions and labels.

Annotation Format (JSONL)

Each entry provides a clean description of the incident and categorical attributes to distinguish between fire and smoke presence.

{
  "image": "fire_cctv_0042.png",
  "description": "A small fire starting inside a metal trash bin on a concrete sidewalk, thin grey smoke rising, high-angle surveillance view.",
  "attributes": {
    "incident_type": "bin_fire",
    "visibility": "visible_smoke_and_flame",
    "ignition_stage": "initial_ignition",
    "environment": "urban_sidewalk"
  }
}

Incident Categories
Category	Description
Bin Fires	Internal ignitions and smoke rising from public or industrial waste containers.
Ground Ignitions	Small-scale paper, cardboard, or debris fires on asphalt and concrete.
Vegetation Smoke	Small spot fires or smoldering cigarettes in grass or public park areas.
Urban Contexts	Alleyways, bus stops, plazas, and commercial exterior zones.
🛠 Use Cases

    Smart City Safety: Automating real-time emergency alerts for public infrastructure.

    Facility Management: Detecting smoking or fire violations in prohibited zones (warehouses, loading docks).

    Early Warning Systems: Reducing emergency response times by identifying "pre-conflagration" events before they spread.

⚖️ Ethics & License

Synthetic Nature: This data is computer-generated by the Simuletic pipeline. It allows for the safe training of emergency models without the inherent danger or ethical concerns of recording real-world accidents or setting intentional fires.

License: CC BY 4.0. You are free to use, share, and adapt this data, provided you give appropriate credit to Simuletic.
📖 Citation

If you use this dataset in your research or production models, please cite:

@dataset{simuletic_cctv_fire_2026,
  author = {Simuletic Team},
  title = {CCTV Smoke & Fire Emergency Detection Dataset},
  year = {2026},
  publisher = {Kaggle},
  url = {[https://simuletic.com/datasets](https://simuletic.com/datasets)}
}