Simuletic/Long_Distance_Wildfire_Smoke_Detection_Dataset

Long-Range Wildfire & Smoke Detection Dataset

Long-distance forest monitoring dataset featuring early smoke plumes and wildfire ignitions across global biomes.

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🧐 Overview

Long-Range Wildfire & Smoke is a specialized open-source synthetic dataset for Computer Vision (CV) tasks focused on Environmental Monitoring and Early Warning Systems.

Detecting a wildfire before it crowns is the most effective way to prevent ecological disaster. This dataset focuses on the long-shot perspective—replicating high-vantage surveillance cameras (tower-mounted or ridge-line cameras) that monitor vast forest expanses. By focusing heavily on smoke plumes (which are visible long before the fire itself), this dataset prepares models for real-world early detection where flames are often obscured by terrain or canopy. Read more: Simuletic Wildfire Dataset Information

🚀 Need more data?

This 200+ image set is a sample from the Simuletic Wildfire Series. We provide hyper-realistic synthetic data to solve the challenge of detecting fires across diverse global landscapes.

• Full Wildfire Dataset: Extensive collections featuring 1500+ images.
• Global Biomes: Scenarios spanning Mediterranean scrub, Southern US pine forests, Asian tropical regions, and Greek highlands. Explore the full library at: simuletic.com/datasets

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✨ Key Features

• Long-Distance Perspective: Specifically captured from simulated high-vantage points (fire towers, mountain peaks) to replicate long-range monitoring.
• Realistic Class Distribution: 90% of the dataset focuses on Smoke, reflecting the most common real-world detection scenario where the fire is not yet visible.
• Global Diversity: Varied terrain and vegetation types, including dry Mediterranean forests, lush Asian canopies, and North American woodlands.
• Privacy-First & Ethical: 100% synthetic. No real forests were burned, and no private property or PII is included, ensuring safe and compliant R&D.

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📊 Dataset Structure

The dataset includes high-resolution synthetic images and a central metadata.jsonl file.

Annotation Format (JSONL)

Annotations include a natural language description and categorical attributes for fire/smoke detection.

{
  "image": "wildfire_long_082.png",
  "description": "A thick plume of grey smoke rising from a dense Mediterranean forest on a distant hillside, high-vantage surveillance view.",
  "attributes": {
    "class": "smoke",
    "distance": "long_range",
    "biome": "mediterranean",
    "visibility": "partial_obscured"
  }
}

Class Map

    Smoke: Visible plumes, wisps, or thick columns rising above the canopy (90% of images).

    Wildfire: Active flames or crowning visible from the surveillance point (10% of images).

🛠 Use Cases

    Early Warning Systems: Automating 24/7 monitoring for forest fire towers and remote stations.

    Drone & Aerial AI: Training models for UAVs patrolling high-risk environmental zones.

    Environmental Protection: Enhancing response times for fire departments and forestry services through automated smoke detection.

⚖️ Ethics & License

Synthetic Nature: This data is computer-generated by the Simuletic pipeline. It allows developers to train wildfire detection models without relying on scarce, low-quality, or ethically sensitive real-world fire footage.

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 project, please cite:
Kodavsnitt

@dataset{simuletic_wildfire_smoke_2026,
  author = {Simuletic Team},
  title = {Long-Range Wildfire & Smoke Detection Dataset},
  year = {2026},
  publisher = {Kaggle},
  url = {[https://simuletic.com/datasets](https://simuletic.com/datasets)}
}

Feedback? Reach out via simuletic.com or the "Issues" tab here on Kaggle.