DOI: 10.5281/zenodo.17047113
title: MultiFire20K: A semi-supervised enhanced large-scale UAV-based dataset for fire monitoring

description: MultiFire20K

MultiFire20K is a UAV-based dataset for fire, classification, segmentation and multi-task learning. It contains 20,500 images extracted from 67 videos, covering diverse urban (build-up) and rural (natural landscape) scenes worldwide.

Dataset Stats- Images: 20,500- Videos: 67- Categories: Fire / Normal- Environments: Urban / Rural

│ Category │ Build-Up │ Natural Landscape  │  Total ││ Fire          │    5,075   │              5,085           │ 10,160 ││ Normal    │    5,110   │              5,230           │ 10,340 ││ Total        │   10,185   │             10,315         │ 20,500 │

Annotations- Manual masks (~10%) created with Fiji and LabelKit.- Pseudo-labels (~90%) via a semi-supervised ensemble (for fire images).- Normal images: RGB only (no masks).

│ Fire Subset            │ Human Ann. │ Pseudo-Labels  │   Total ││ Build-Up (Urban)   │        510       │          4,565        │   5,075 ││ Rural (Landscape)  │        515       │          4,570        │   5,085 ││ TOTAL                    │      1,025       │          9,135        │  10,160 │

 

File Naming & TraceabilityEach image encodes its source video and frame number:  `FramesV1_1000.jpg` → extracted from Video 1, frame 1000.

Image & Mask Formats- Images: `.jpg` (various resolutions; mean ≈ 1147×636, range 426–1920 × 210–1080)- Masks: `.tif` (single-channel, pixel-aligned with the image, used for *fire* images only)

Folder StructureMultiFire20K/├─ Annotations/│  ├─ Fire_Rural_Annotate/        # fire rural: image.jpg + mask.tif│  ├─ Fire_Rural_Annotate_RGB/    # fire rural RGB only│  ├─ Fire_Urban_Annotate/        # fire urban: image.jpg + mask.tif│  ├─ Fire_Urban_Annotate_RGB/    # fire urban RGB only├─ Train/                         # FR, FU, NR, NU subfolders├─ Val/                  # FR, FU, NR, NU subfolders├─ Test/               # FR, FU, NR, NU subfolders└─ data_structure.csv            # metadata (label_type, fire_type, category, split)```

Split codes:  > FR = Fire Rural, FU = Fire Urban, NR = Normal Rural, NU = Normal Urban.  > Rural is also known as Natural Landscape; Urban as Build-Up.

Note: In FR and FU (fire) folders you’ll find image + mask pairs (.jpg + .tif). In NR and NU (normal) folders there are images only (.jpg, no masks). Use data_structure.csv to check whether a fire mask is manual or pseudo.

Metadata (`data_structure.csv`)Columns:- `image_name` — filename (e.g., `FramesV11_24900.jpg`)- `label_type` — `manual` (human) or `pseudo` (generated)- `fire_type` — `fire` or `normal`- `category` — `rural` (natural landscape) or `urban` (build-up)- `split` — `train`, `val`, `test`

Use this table to:- filter manual vs pseudo masks,- select fire vs normal,- choose rural vs urban,- load the official split.

Recommended Usage

Classification- Labels: `fire_type` (binary) and optionally `category` (urban/rural).- Use FR/FU/NR/NU folders or `data_structure.csv` for labels and splits.

Segmentation- Pair each `image.jpg` with `mask.tif` of the same basename (fire images only).- Prefer manual** masks for evaluation; pseudo-labels can be used for training at scale.

CitationIf you use MultiFire20K, please cite:@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}}