| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - wildfire |
| - remote-sensing |
| - viirs |
| - earth-observation |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: next_day_v3/splits/train.jsonl |
| - split: validation |
| path: next_day_v3/splits/val.jsonl |
| - split: test |
| path: next_day_v3/splits/test.jsonl |
| --- |
| |
| # FireComp: Next-Day Fire Spread |
|
|
| Global benchmark for next-day wildfire spread prediction from 375 m VIIRS |
| active-fire detections, with ERA5 weather, GFS forecasts and Alpha Earth |
| terrain embeddings. 256×256 patches, 9 regions, 2017–2025. |
|
|
| Code, documentation, loaders and paper: **https://github.com/justuskarlsson/FireComp** |
|
|
| ## Paper |
|
|
| This dataset accompanies the FireComp paper, **accepted and presented at GCPR 2026** |
| (German Conference on Pattern Recognition). It is released so the benchmark results |
| in that paper can be reproduced and built upon. See the GitHub repository for the |
| citation entry. |
|
|
| ## Contents |
|
|
| | Path | Description | |
| |---|---| |
| | `next_day_v3/` | Main dataset: 8 HDF5 shards (`dataset_*.h5`, zstd) + per-shard metadata (`dataset_*.json`), `samples.json`, `stats.json` (normalization) | |
| | `next_day_v3/splits/{train,val,test}.jsonl` | Flat sample index per split (stratified temporal 60/15/25 within each region) — what the dataset viewer shows | |
| | `next_day_v3_case_study/` | Small case-study subset used for paper figures | |
| | `regions/` | Region definitions (`wildfire_regions.json` / `.tif`) | |
| | `fire_areas.npz` | Per-fire spatial area lookup (fire-size stratification) | |
| | `vnp14_fires/vnp14_fires_2012.h5` | VIIRS fire-event index, 2012–2025 (~55 GB) — see below | |
|
|
| ### `vnp14_fires_2012.h5` |
|
|
| Individual VNP14IMG active-fire detections clustered into **fire events** by a |
| spatio-temporal BFS, which is what defines the `fire_id` used everywhere else in |
| the benchmark. Not needed to train on the dataset above, but required for most of |
| the analysis and visualization: per-fire statistics, size/region breakdowns, |
| wild-vs-tame classification, fire-shape (solidity) analysis and the paper figures. |
|
|
| | Group | Contents | |
| |---|---| |
| | `stats/`, `stats_{10,100,1000,10000}/` | Per-fire records (`id`, `start_date`, `end_date`, bbox, `country`, `avg_xy_neighbors`, `ignition_ratio`, …), the suffix being a minimum-detection-count filter | |
| | `pixels/` | Every detection: `fire_id`, `date`, position, `cls`, `ignition`, `image_id` | |
| | `projection_by_fire/`, `projection_by_t/` | Detections rasterized per fire / per timestep (`projection_by_fire` covers fires with ≥100 detections) | |
| | `images/`, `meta/` | Source granule index and archive date range | |
|
|
| ## Usage |
|
|
| Clone/download into `data/` of the GitHub repo and follow its README. |
| Reading the shards requires `h5py` and `hdf5plugin` (zstd filter). |
|
|
