| --- |
| 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
