FireDataLoader

A tool for downloading and processing wildfire-related geospatial data from multiple sources for machine learning and analysis.

Data Sources

Dataset	Description	Resolution
FEDS25MTBS	Fire perimeter time series	375m
USGS 3DEP	Elevation	1m
LANDFIRE	Canopy Bulk Density (CBD), Canopy Cover (CC)	30m
HRRR	Weather: humidity (r2), wind (u10, v10)	3km
Global Building Atlas	Building heights	3m
ESA WorldCover	Land cover classification	10m
Tree Canopy LAI	Leaf Area Index	10m
NAIP/Sentinel-2	Satellite imagery (RGB)	1m/10m
Hillshade	Terrain visualization (from elevation)	1m
Global WUI	Wildland-Urban Interface classification	10m

Installation

Requires Python 3.10+ and uv.

uv sync

Prerequisites

Google Earth Engine Setup

Before running, configure your GEE project:

# Authenticate (one-time, opens browser)
earthengine authenticate

# Set your project ID
earthengine set_project YOUR-PROJECT-ID

To get a project ID:

1. Go to Google Cloud Console
2. Create or select a project with Earth Engine enabled
3. Copy the project ID

Quick Start

# Process a fire event
python main.py CA3859812261820171009

# With verbose output
python main.py CA3859812261820171009 -v

Usage

python main.py <event_id> [options]

Options

Option	Description	Default
-r, --resolution	Spatial resolution (meters)	30
-b, --buffer	Buffer around fire bounds (meters)	20
-c, --crs	Target coordinate reference system	EPSG:5070
-o, --output_dir	Output directory	output
-t, --interpolation	Intermediate frames between timesteps	0
--herbie_cache_dir	HRRR cache directory	datasets/herbie
-v, --verbose	Enable verbose logging	False

Examples

# High resolution processing
python main.py CA3859812261820171009 -r 10 -v

# With temporal interpolation (3 intermediate frames)
python main.py CA3859812261820171009 -t 3

# Custom output directory
python main.py CA3859812261820171009 -o ./my_output

Output

Data is saved as .npy files in output/<event_id>/:

output/CA3859812261820171009/
├── task_info.npy         # Processing configuration
├── burn_perimeters.npy   # Fire perimeter time series
├── elevation.npy         # Terrain elevation
├── cbd.npy               # Canopy Bulk Density
├── cc.npy                # Canopy Cover
├── r2.npy                # Relative humidity
├── u10.npy               # Wind U component
├── v10.npy               # Wind V component
├── building_height.npy   # Building heights
├── landcover.npy         # Land cover classes
├── lai.npy               # Leaf Area Index
├── satellite.npy         # RGB satellite imagery (NAIP/Sentinel-2)
├── hillshade.npy         # Terrain hillshade visualization
└── wui.npy               # Wildland-Urban Interface classification

Visualizing Data

# Plot all layers overview (saves to file)
python plot_data.py CA3859812261820171009

# Plot and display interactively
python plot_data.py CA3859812261820171009 --show

# Plot time series frames (e.g., burn perimeters)
python plot_data.py CA3859812261820171009 -t burn_perimeters

# Plot time series and display interactively
python plot_data.py CA3859812261820171009 -t burn_perimeters --show

Loading Data

from main import load_numpy

# Load a data file
data = load_numpy('output/CA3859812261820171009/elevation.npy')
print(data.name)        # 'elevation'
print(data.data[0].shape)  # (height, width)
print(data.unit)        # 'm'

Using Schemas Externally

from schemas import FireInfo, TaskInfo, DataWithMetadata
from main import get_fire_info, get_task_info

# Get fire information
fire = get_fire_info('CA3859812261820171009')
print(f"{fire.name}: {fire.acres_burned} acres")

# Create task configuration
task = get_task_info(fire, resolution=30)
print(f"Grid: {task.shape}, CRS: {task.crs}")

Available Fire Events

Fire events are listed in datasets/FEDS25MTBS/fireslist2012-2023.csv. Event IDs follow the pattern: {STATE}{LAT}{LON}{DATE} (e.g., CA3859812261820171009).

FEDS25MTBS Dataset

The Fire Event Data Suite (FEDS) provides half-daily fire perimeter time series derived from VIIRS satellite observations using an object-based tracking system.

Data Source

• Publication: Balch, J.K. et al. (2022). California wildfire spread derived using VIIRS satellite observations and an object-based tracking system. Scientific Data. https://doi.org/10.1038/s41597-022-01343-0
• Data Repository: figshare (public data for 2012-2020)
• Extended Data: Our dataset (2012-2023) was accessed via request to the authors
• Temporal Coverage: 2012-2023 fire seasons
• Resolution: 375m (VIIRS native resolution)

Data Setup

Place the GeoPackage files in datasets/FEDS25MTBS/ organized by year:

datasets/FEDS25MTBS/
├── fireslist2012-2023.csv    # Fire event metadata
├── 2012/
│   └── CA3245811923420120801.gpkg
├── 2013/
│   └── ...
├── ...
└── 2023/
    └── ...

Each .gpkg file contains the perimeter layer with fire boundary polygons at each timestep.

Event ID Format

Event IDs follow the pattern: {STATE}{LAT}{LON}{DATE}

• STATE: 2-letter state code (e.g., CA)
• LAT: Latitude × 100 (5 digits, e.g., 38598 for 38.598°)
• LON: Longitude × 100 (5 digits, e.g., 12261 for -122.61°)
• DATE: Fire start date as YYYYMMDD (e.g., 20171009)

Example: CA3859812261820171009 = California fire at (38.598°N, 122.618°W) starting October 9, 2017

ESA WorldCover Dataset

The ESA WorldCover dataset provides global land cover classification at 10m resolution based on Sentinel-1 and Sentinel-2 data.

Data Source

• Provider: European Space Agency (ESA)
• Data Access: Google Earth Engine (ESA/WorldCover/v200)
• Temporal Coverage: 2021
• Resolution: 10m
• Coverage: Global

Land Cover Classes

Value	Class	Color
10	Tree Cover	Dark Green
20	Shrubland	Orange/Yellow
30	Grassland	Yellow
40	Cropland	Pink
50	Built-up	Red
60	Bare/Sparse Vegetation	Gray
70	Snow and Ice	White
80	Permanent Water Bodies	Blue
90	Herbaceous Wetland	Teal
95	Mangroves	Green
100	Moss and Lichen	Beige

Data Access

This dataset is automatically downloaded from Google Earth Engine during processing. No manual setup required.

Global WUI Dataset

The Global Wildland-Urban Interface (WUI) dataset maps where buildings and wildland vegetation meet or intermingle at 10m resolution globally.

Data Source

• Publication: Schug, F. et al. (2023). The global wildland–urban interface. Nature. https://doi.org/10.1038/s41586-023-06320-0
• Data Repository: Available from SILVIS Lab, University of Wisconsin-Madison
• Temporal Coverage: ca. 2020
• Resolution: 10m
• Projection: EQUI7 Azimuthal Equidistant

WUI Classes

Value	Class	Description
1	Forest/Shrub/Wetland Intermix WUI	Buildings intermixed with forest/shrub/wetland vegetation
2	Forest/Shrub/Wetland Interface WUI	Buildings adjacent to forest/shrub/wetland vegetation
3	Grassland Intermix WUI	Buildings intermixed with grassland vegetation
4	Grassland Interface WUI	Buildings adjacent to grassland vegetation
5	Non-WUI: Forest/Shrub/Wetland	Forest/shrub/wetland without WUI
6	Non-WUI: Grassland	Grassland without WUI
7	Non-WUI: Urban	Urban areas without wildland interface
8	Non-WUI: Other	Other land cover types

Data Setup

The Global WUI data uses the EQUI7 tiling grid. Download required tiles and place them in datasets/globalwui/:

datasets/globalwui/
├── X0065_Y0040/
│   └── WUI.tif
├── X0066_Y0040/
│   └── WUI.tif
└── ...

The script automatically identifies which tiles are needed based on the fire event location.

License

See LICENSE.