README.md

metadata

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).