| | --- |
| | 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. |
| |
|
| |  |
| |
|
| | ## 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**. |
