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}}