Dataset card: fix formatting, real sample counts, links to the full dataset, licence text

README.md

@@ -2,105 +2,139 @@
 license: cc-by-nc-4.0
 language:
 - en
-pretty_name: "Long-Distance Wildfire & Smoke Detection Dataset"
+pretty_name: Long-Distance Wildfire & Smoke Detection Dataset (free sample)
 task_categories:
 - object-detection
 tags:
 - synthetic
-- cctv
 - surveillance
-- security
 - computer-vision
 - wildfire-detection
 - smoke-detection
+- forest-fire-detection
+- early-warning
+- small-object-detection
 - yolo
+- vlm
 size_categories:
 - n<1K
 ---
 
-<!-- simuletic-canonical -->
-> **This is the free sample of the [Long-Distance Wildfire & Smoke Detection Dataset](https://simuletic.com/datasets/long-distance-wildfire-smoke-detection-dataset) by [Simuletic](https://simuletic.com/).** Simuletic makes synthetic CCTV and security datasets for computer vision. The full dataset, its specification and licence (a one-time research or commercial licence) are on the dataset page.
->
-> **Full dataset and licence: https://simuletic.com/datasets/long-distance-wildfire-smoke-detection-dataset**
->
-> The images here are a sample under the licence in this card's metadata; the full dataset is licensed separately.
+# Long-Distance Wildfire & Smoke Detection Dataset (free sample)
 
-# Long-Range Wildfire & Smoke Detection Dataset
+A free sample of **239 synthetic images of distant wildfire smoke, seen from elevated fire-watch cameras**, with YOLO bounding boxes for `smoke` and `wildfire` and a text caption per image, for training and testing **wildfire and smoke detection** models. Most images show smoke only, which is how an early wildfire usually looks from far away. Made by Simuletic, which builds synthetic CCTV datasets for computer vision.
 
-### Long-distance forest monitoring dataset featuring early smoke plumes and wildfire ignitions across global biomes.
+**Full dataset and licence:** [simuletic.com/datasets/long-distance-wildfire-smoke-detection-dataset](https://simuletic.com/datasets/long-distance-wildfire-smoke-detection-dataset?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=wildfire)
 
----
+| | Free sample (this page) | [Full dataset](https://simuletic.com/datasets/long-distance-wildfire-smoke-detection-dataset?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=wildfire) |
+|---|---|---|
+| Images | 239 PNG, 1,024 × 1,024 px (189 smoke only, 50 with flames visible) | 1,500, from first ignition to established plume |
+| Classes | `smoke`, `wildfire` | `smoke`, `wildfire` |
+| Labels | YOLO `.txt` + one JSONL of text captions | YOLO |
+| Camera view | elevated long-range views over forest, hills and valleys | fire-watch and lookout-tower views; boreal, chaparral, eucalyptus, mixed conifer and alpine; dawn to dusk; clear to hazy |
+| Licence | CC BY-NC 4.0 | one-time licence: research $149 / commercial $590 per 1,000 images |
 
-## 🧐 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**.
+## What's in the images
 
-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)
+Early wildfires seen from far away, the way a fire-watch camera on a tower or ridge sees them. Smoke usually shows long before flames, and flames are often hidden by terrain or trees, so most images show smoke only: a thin grey trail or a small plume rising from a ridge, valley or hillside. In 50 of the 239 images a small flame or orange glow is visible at the base of the smoke. Plumes are often tiny, low-contrast objects in a wide landscape.
 
-### 🚀 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)**
+Landscapes in the sample include forested valleys, rocky mountain ridges, dry Mediterranean pine forest, olive groves and scrub, limestone cliffs, dense conifer canopy, eucalyptus savanna and rural land with patches of woodland. The images come from 48 scene setups.
 
----
+## Classes and boxes
 
-## ✨ 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.
+| Class | YOLO id | What it marks | Boxes in sample |
+|---|---|---|---|
+| `smoke` | 0 | smoke plumes, wisps and columns | 255 |
+| `wildfire` | 1 | visible flames | 71 |
 
----
+Smoke is boxed in 238 of the 239 images and flames in 47.
+
+## Files
 
-## 📊 Dataset Structure
-The dataset includes high-resolution synthetic images and a central `metadata.jsonl` file.
+```text
+Long-Distance  Wildfire & Smoke Dataset/      # note: two spaces after "Long-Distance"
+  images/                                     # 239 PNG images (var1_early_wildfire_smoke_only_img1.png, ...)
+    wildfire_detection_dataset.jsonl          # one text caption per image
+  labels/                                     # one YOLO .txt per image, same file names
+  data.yaml                                   # class names: 0 smoke, 1 wildfire
+```
 
-### Annotation Format (JSONL)
-Annotations include a natural language description and categorical attributes for fire/smoke detection.
+Each YOLO label line is `class_id x_center y_center width height`, normalised to 0–1. This smoke box (`var1_early_wildfire_smoke_only_img1.txt`) covers about 4% of the image width:
+
+```text
+0 0.492574 0.364480 0.039604 0.068069
+```
+
+`wildfire_detection_dataset.jsonl` has one line per image in chat format: a fixed question, and an answer with the alert status (smoke only, or flame visible), the location and what is seen. Use it to fine-tune vision-language models that write alert messages:
 
 ```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"
-  }
-}
+{"image": "var1_early_wildfire_smoke_only_img1.png", "conversations": [{"role": "user", "content": "Analyze this long-range optical sensor feed for early signs of wildfire."}, {"role": "assistant", "content": "Alert Status: Early Wildfire Smoke Only. Location: a deep forested valley under a clear blue sky. Observation: a subtle trail of grey smoke drifting up from a remote rocky ridge. Only smoke visible."}]}
+```
+
+## Quick start (Ultralytics YOLO)
+
+Create a `data.yaml` next to the dataset folder (quote the path, it contains spaces):
+
+```yaml
+path: "Long-Distance  Wildfire & Smoke Dataset"
+train: images
+val: images   # small sample: use the same images, or split them yourself
+names:
+  0: smoke
+  1: wildfire
+```
+
+Then train:
+
+```python
+from ultralytics import YOLO
+
+model = YOLO("yolo11n.pt")
+model.train(data="data.yaml", epochs=30, imgsz=640)
+```
+
+## Use cases
+
+- Wildfire early-warning camera networks and AI-assisted lookout towers
+- Forest and national park monitoring
+- Monitoring utility lines and the edge between towns and wildland
+- Vision-language models that describe smoke and fire in an alert, using the captions
+- Benchmarking detectors on small, distant, low-contrast objects
 
-Class Map
+## How the images were made
 
-    Smoke: Visible plumes, wisps, or thick columns rising above the canopy (90% of images).
+The images are fully synthetic (computer-generated) by [Simuletic](https://simuletic.com/?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=wildfire): no real people, places or incidents are depicted, so there are no privacy or consent issues. 
 
-    Wildfire: Active flames or crowning visible from the surveillance point (10% of images).
+**Limitations.** Synthetic images do not cover every camera, lens, compression artefact or scene you will meet in deployment. Validate on your own footage, and for production combine synthetic and real data. This sample has 239 images from 48 scene setups; the full dataset has 1,500 images across more biomes, times of day and weather.
 
-🛠 Use Cases
+## Full dataset and custom data
 
-    Early Warning Systems: Automating 24/7 monitoring for forest fire towers and remote stations.
+The full **[Long-Distance Wildfire & Smoke Detection Dataset](https://simuletic.com/datasets/long-distance-wildfire-smoke-detection-dataset?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=wildfire)** has 1,500 images, YOLO, classes smoke, wildfire. Licence: research $149 / commercial $590 per 1,000 images. Larger sets, other scenes, camera angles or classes: see [custom data](https://simuletic.com/custom-data?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=wildfire) or write to contact@simuletic.com.
 
-    Drone & Aerial AI: Training models for UAVs patrolling high-risk environmental zones.
+This free sample is also on [Kaggle](https://www.kaggle.com/datasets/simuletic/long-distance-wildfire-and-smoke-detection-dataset).
 
-    Environmental Protection: Enhancing response times for fire departments and forestry services through automated smoke detection.
+## More synthetic datasets from Simuletic
 
-⚖️ Ethics & License
+- [CCTV Smoke & Fire Emergency Detection Dataset](https://huggingface.co/datasets/Simuletic/CCTV-Smoke-Fire-Emergency-Detection-Dataset)
+- [Airport Drone Threat & Safety Dataset](https://huggingface.co/datasets/Simuletic/Airport_Drone_Threat_And_Safety_Dataset)
+- [CCTV Fall & Incident Detection Dataset](https://huggingface.co/datasets/Simuletic/CCTV_Incident_Dataset_Fall_Lying_Down_Detection)
+- All datasets: [simuletic.com/datasets](https://simuletic.com/datasets?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=wildfire) · [Hugging Face](https://huggingface.co/Simuletic) · [Kaggle](https://www.kaggle.com/simuletic) · [GitHub](https://github.com/Simuletic)
 
-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.
+## Licence
 
-License: CC BY 4.0. You are free to use, share, and adapt this data, provided you give appropriate credit to Simuletic.
-📖 Citation
+This free sample is licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/): you may share and adapt it for non-commercial purposes, as long as you credit Simuletic. The full dataset is sold under a separate one-time research or commercial licence.
 
-If you use this dataset in your research or project, please cite:
-Kodavsnitt
+## Citation
 
-@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)}
+```bibtex
+@misc{simuletic_wildfire_2026,
+  author       = {{Simuletic}},
+  title        = {Long-Distance Wildfire & Smoke Detection Dataset},
+  year         = {2026},
+  publisher    = {Simuletic},
+  howpublished = {\url{https://huggingface.co/datasets/Simuletic/Long_Distance_Wildfire_Smoke_Detection_Dataset}},
+  note         = {Free sample. Full dataset: \url{https://simuletic.com/datasets/long-distance-wildfire-smoke-detection-dataset}}
 }
+```
 
-Feedback? Reach out via simuletic.com or the "Issues" tab here on Kaggle.
+Questions or feedback: contact@simuletic.com