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Datasets:
matjs
/
ignis

Tasks:
Image Segmentation
Languages:
English
Size:
n<1K
Tags:
geografy
wildfire
nature
preservation
License:
Dataset card Files
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ignis
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---
license: cc-by-4.0
task_categories:
- image-segmentation
language:
- en
tags:
- geografy
- wildfire
- nature
- preservation
pretty_name: IGNIS - Intelligent Geospatial Network for Incendiary Surveillance
size_categories:
- n<1K
---

# IGNIS - Intelligent Geospatial Network for Incendiary Surveillance

A dataset for **image segmentation of wildfires** in satellite/aerial imagery. The dataset contains **paired images and labels**, where each label highlights wildfire-affected regions.

![IGNIS Dataset Sample Image](assets/cover1.png)

## Dataset Summary

This dataset was created to support research in **wildfire detection, monitoring, and environmental risk assessment**. It can be used for training and evaluating segmentation models.

* **Task:** Image Segmentation
* **Domain:** Remote sensing / Environmental monitoring
* **License:** CC BY 4.0

## Supported Tasks

* **Image Segmentation** – Identify wildfire regions pixel-by-pixel.
* **Potential Applications:**

  * Early wildfire detection
  * Environmental monitoring
  * Risk modeling and prevention systems

## Dataset Structure

### Data Splits

The dataset is divided into:

* `train`
* `validation`
* `test`

## Data Fields

* **image** (`Image`) – RGB image
* **label** (`Label`) – TXT file containing coordinates for the polygons following YOLOv11 format

Example:

```
{
  "image": "train/images/image_001.jpg",
  "label": "train/labels/image_001.txt"
}
```

## Dataset Creation

### Motivation

Wildfires are an increasing threat worldwide. This dataset was built to help researchers and engineers develop segmentation models that can detect wildfire-affected areas in aerial/satellite imagery.

This dataset is originally a personal project, but anyone with expert knowledge in meteorological, geographic, geophisical and related areas might feel free to reach out and help expand the dataset and increase its quality.

### Source Data

* **Collection Process:** Images were sourced from open satellite/aerial datasets.
* **Annotation Process:** Masks were generated using a mix between manual labelling and automatic polygon generation thanks to Roboflow's tools.

### Annotations

* **Annotation Guidelines:** Each class is labeled as:

  * 0 → Burned Ground (burnt)
  * 1 → Smoke Cloud (smoke_cloud)
  * 2 → Smoke Column (smoke_column)
  * 3 → Wildfire (wildfire)

## Licensing Information

* **Dataset License:** CC BY 4.0

## Citation

If you use this dataset, please cite:

```
@dataset{ignis,
  title = {Intelligent Geospatial Network for Incendiary Surveillance},
  author = {Matheus J. G. Silva},
  year = {2025},
  url = {https://huggingface.co/datasets/matjs/ignis}
}
```

## Acknowledgements

* [NASA FIRMS - Fire Information for Resource Management System](https://firms.modaps.eosdis.nasa.gov/)
* [NASA Earth Observatory](https://earthobservatory.nasa.gov)
* Inspired by the growing need for **AI-assisted wildfire monitoring**.