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README.md

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-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+---
+# Long-Range Wildfire & Smoke Detection Dataset
+
+### Long-distance forest monitoring dataset featuring early smoke plumes and wildfire ignitions across global biomes.
+
+---
+
+## 🧐 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](https://simuletic.com/datasets/long-distance-wildfire-smoke-detection-dataset)
+
+### 🚀 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](https://simuletic.com/datasets)**
+
+---
+
+## ✨ 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.
+
+---
+
+## 📊 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.
+
+```json
+{
+  "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.