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chaseungjoon/wildfire-korea-episodes-300m

South Korean Wildfire Episodes Dataset (300 m Grid) A geospatial dataset of South Korean wildfire progression combining NASA FIRMS (VIIRS/MODIS) fire detections with static environmental layers (DEM, landcover, slope, NDVI, forest structure, etc.). Tiles are preprocessed into a 300 m spatial grid and segmented into temporal wildfire episodes suitable for reinforcement-learning–based wildfire spread prediction research. Overview This dataset provides a unified… See the full description on the dataset page: https://huggingface.co/datasets/chaseungjoon/wildfire-korea-episodes-300m.

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South Korean Wildfire Episodes Dataset (300 m Grid)

A geospatial dataset of South Korean wildfire progression combining NASA FIRMS (VIIRS/MODIS) fire detections with static environmental layers (DEM, landcover, slope, NDVI, forest structure, etc.).

Tiles are preprocessed into a 300 m spatial grid and segmented into temporal wildfire episodes suitable for reinforcement-learning–based wildfire spread prediction research.

Overview

This dataset provides a unified, model ready representation of wildfire behavior across the Korean Peninsula from 2015–2025. Each episode contains:

  • —A temporal sequence of environmental feature tensors (16 channels)
  • —Corresponding fire spread masks at each timestep
  • —Pre-normalized and aligned geospatial layers
  • —Episode segmentation based on FIRMS temporal-spatial clustering

This dataset directly supports training RL models such as A3C-LSTM wildfire spread predictors.

Dataset Structure

Each episode is stored as a compressed .npz file:

bash
episode_XXXX.npz
├── states # (T, 16, 30, 30) environmental grids over time
└── fire_masks # (T, 30, 30) binary wildfire progression masks

Where:

  • —T = number of timesteps in the episode
  • —16 channels include:
  • —DEM (elevation, slope, aspect)
  • —Weather (temp, humidity, wind speed, wind direction, precip, pressure, cloud, visibility, dew point)
  • —NDVI
  • —FSM (forest susceptibility or categorical forest-type channels)

Data Fields

`states`

Shape: (T, 16, 30, 30) Contains the full spatiotemporal feature tensor used by RL agents.

`fire_masks`

Shape: (T, 30, 30) Binary wildfire mask where:

  • —1 = active fire
  • —0 = non-burning cell

Preprocessing Pipeline

  1. 1.FIRMS VIIRS/MODIS fire detections collected (2015–2025)
  2. 2.Sliding Window Scan clustering applied to group detections into separate wildfire episodes
  3. 3.All geospatial layers resampled to 300 m grid resolution
  4. 4.Static layers embedded as multi-channel raster tiles
  5. 5.Episode .npz constructed from:
  6. 6.FIRMS-derived fire mask sequence
  7. 7.Environmental tensors per timestep

Intended Use

This dataset is designed for:

  • —Reinforcement-learning wildfire spread models (A3C, A2C, LSTM-based agents)
  • —Spatiotemporal forecasting research
  • —Geospatial ML and uncertainty modeling
  • —Comparative experiments with CNN/LSTM/U-Net wildfire models

Limitations

  • —FIRMS detections have temporal gaps and positional uncertainty
  • —Environmental features are aggregated to 300 m resolution
  • —Episode segmentation depends on clustering thresholds
  • —Dataset is not meant for operational wildfire forecasting without additional calibration

Citation

If you use this dataset, please cite:

Cha, S.J. (2025). Korean Peninsula Wildfire Episodes Dataset (300 m Grid).
WildFirePrediction Project, Chung-Ang University.
chaseungjoon/wildfire-korea-episodes-300m · CoolFace