metadata
license: cc-by-4.0
task_categories:
- image-segmentation
language:
- en
tags:
- fire
- aerial
size_categories:
- 10K<n<100K
MultiFire20K Dataset
Overview
MultiFire20K is a UAV-based dataset for fire monitoring, designed for image classification, segmentation, and multi-task learning. It contains 20,500 images extracted from 67 UAV videos captured across diverse urban and rural environments worldwide.
Statistics
| Split | Images |
|---|---|
| Train | 14,350 |
| Validation | 3,075 |
| Test | 3,075 |
| Total | 20,500 |
Classes
- Fire
- Normal
Environment Types
- Urban (Build-Up)
- Rural (Natural Landscape)
Source
Original dataset:
https://zenodo.org/records/17047113
Citation
@article{shianios2025multifire20k,
title={MultiFire20K: A semi-supervised enhanced large-scale UAV-based benchmark for advancing multi-task learning in fire monitoring},
author={Shianios, Demetris and Kolios, Panayiotis and Kyrkou, Christos},
journal={Computer Vision and Image Understanding},
volume={254},
pages={104318},
year={2025},
publisher={Elsevier}
}