Wildfire Forecasting from Satellite Imagery

OxAI Labs Earth and Space Project

Initial setup

Conda environment

We recommend installing conda to manage the python environment.

conda create --name <your-env-name> python=3.7 conda activate <your-env-name>

Install Dependencies

pip install -r requirements_minimum.txt

Set up Google Earth Engine

You need to sign up to use Google Earth Engine in order to use this free API. Please refer to the README for Google Earth Engine for more details.

Set up Sentinelhub

This is optional, and you may not require it if you are able to set up Google Earth Engine. Please refer to the README for Sentinel Hub for more details.

Datasets

FPA-FOD Dataset

Download FPA_FOD_20170508.sqlite and place it in the resources/fpa_fod/data_dir/ directory.

MODIS fire archive

Follow the instructions in the MODIS Fire directory.

GlobFire Dataset

Follow the instructions in the GlobFire directory.

Models

The code in the models directory is at an experimental stage, and is not intended to be used at its current state. We developed this code to test a simple CNN model to classify images that capture wildfire and those which don't. While the result was promising, we were only able to demonstrate this for a small dataset with obvious wildfire images.

Please see here for more discussion on the challenges of approaches that use machine learning for wildfire identification.

Web app

There are optional setup instructions required only if you want to try out our Django webapp to visualise the map.

Errors

If you are encountering errors on Mac such as

Attempting to bind to HOST environment variable

this medium article might help you solve the problem. Set your HOST variable to localhost in bash.

You may encounter installation issues with GeoPandas on Windows. Try conda install -c conda-forge geopandas If that doesn't work, follow this instruction for Windows and install GDAL, Fiona, pyproj, rtree, and shapely from Gohlke (unofficial repository for Windows binaries).