--- license: cc-by-nc-4.0 language: - en pretty_name: Long-Distance Wildfire & Smoke Detection Dataset (free sample) task_categories: - object-detection tags: - synthetic - surveillance - computer-vision - wildfire-detection - smoke-detection - forest-fire-detection - early-warning - small-object-detection - yolo - vlm size_categories: - n<1K --- # Long-Distance Wildfire & Smoke Detection Dataset (free sample) 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. **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 | ## What's in the images 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. 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 | 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 ```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 ``` 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": "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 ## How the images were made 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. **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. ## Full dataset and custom data 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. This free sample is also on [Kaggle](https://www.kaggle.com/datasets/simuletic/long-distance-wildfire-and-smoke-detection-dataset). ## More synthetic datasets from Simuletic - [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) ## Licence 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. ## Citation ```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}} } ``` Questions or feedback: contact@simuletic.com