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Wildfire Prediction

Canada Wildfire Prediction Using Deep Learning.
The objective is to predict the future fire occurrences for the next year given various inputs from the past year and current year.
The problem of wildfire prediction is defined as semantic segmentation problem here meaning each pixel is assigned a probability between 0 and 1 of being the class fire.
The type of input data has an impact on the temporal extent chosen as input to the model.
For instance, vegetation data prior to the period to predict is used while weather data and any data that is not affected by fires is taken from the period to predict.
As an example, to predict the period 2023, we use vegetation data from 2022 and weather data from 2023.
The objective of this project is to demonstrate the potential of deep learning in predicting wildfires across Canada.
The model developed here serves as a proof of concept and is not yet fully optimized, its goal is to offer valuable insights into how advanced machine learning techniques can be applied to wildfire prediction.

Results

UNet Model (latest model) 2023 Predicted Fire Hazard Map (Test Set) 2023 Ground Truth (After QC)
2023 Predicted Fire Hazard Map
2023 Ground Truth
Predicted Fire Hazard Map Satellite Image
Kamloops Predicted Fire Hazard Map
Kamloops (city area with expected high probability)
Kamloops Satellite Image
Kamloops
Kelowna Predicted Fire Hazard Map
Kelowna (city area with expected high probability)
Kelowna Satellite Image
Kelowna
Vancouver Predicted Fire Hazard Map
Vancouver (city area with expected low probability)
Vancouver Satellite Image
Vancouver

Data

Boundaries

Input Data

The following data were used as inputs for the model.
Each input data is stacked to create the final input data for the model.
For data not directly affected by fires (e.g., weather data), we use data from the current year, while for data affected by fires (e.g., vegetation data), we use data from the previous year.
For example, to predict wildfires in 2005, we use weather data from 2005 and vegetation data from 2004.

Dynamic Input Data

Dynamic input data changes over time and is updated on a daily, weekly, bi-weekly, monthly, or yearly basis, depending on the dataset.

Vegetation Data

Weather Data

Fire and Thermal Data

Static Input Data

Static input data does not change over time or is available as a single time slice.

Elevation Data

Water Bodies Data

Data Aggregation

  • All the data that is not already yearly based is aggregated to have a yearly temporal granularity.

Target

  • The target of the model is fire polygons which represent fire occurrence for a specific area at a given time.
  • Currently the model is trained to predict the future wildfire occurrences for the next year.
  • A probability between 0 and 1 is assigned to each pixel where 1 means the model predicted that there is a 100% probability that the area defined by this pixel will burn in the next year.

End-To-End Pipeline

Prerequisites

Conda/Micromamba

Create Virtual Environment

  • Create a new environment using the environment.yaml file from this repository (see this guide).
  • Activate your new environment (see this guide).

NASA Earthdata Account

CDS Account

Pipeline Steps

Download Data

Here is an overview of how the data is download for each data source and the various steps involved (not including resume logic and low level details) :

The first step of the pipeline is to download the data that will be used for training the model.
To do so, one only needs to execute one script, make sure you are at the root of this repository in your terminal.

  1. Setup environment variables in a terminal :

    export NASA_EARTH_DATA_USER=<YOUR USERNAME>

    eg: export NASA_EARTH_DATA_USER=alexandrebrown

    export NASA_EARTH_DATA_PASSWORD="<YOUR_PASSWORD>"

    eg: export NASA_EARTH_DATA_PASSWORD="mypassword123"

    export PYTHONPATH=$(pwd)/src

  2. Edit the configuration file under config/download_data.yaml (or leave as-is) so that you it matches what you want to download (you can also leave everything as-is and only change the year/month range).

    One thing to note is that the logs.nasa_earth_data_logs_folder_path path must be null if you do not wish to resume a previous execution of the download script for the NASA Earthdata or if it is your first time running the script. If you executed the download_data script but an issue occurred (eg: NASA servers went down), check under the logs/ folder for the path to the auto-generated log file. You can pass in the path to the log folder to resume from it (eg: logs/download_data/20240711-081839 will resume using the log file inside logs/download_data/20240711-081839).

    To learn more about NASA Earthdata API and what products and layers mean, see the notebook under experiments/explore_nasa_earth_data.ipynb.

    Each product can have one or more layers, the download_data config allows us to specify which products and which layers from each product to download. The layer name must match the exact layer name (see the exporation notebook for more details on how to get this value).

  3. Run the download_data script :

    python download_data.py

    Note : If an error occurred during the execution (eg: third party server went down/download failed), you can avoid re-submitting processing requests that were already sent before the crash. To do so, look under logs/download_data and copy the path to the folder that was generated and put this path under logs.nasa_earth_data_logs_folder_path.
    For instance, if I have the following : logs/download_data/20240711-081839 from my previous execution, then I would update the config/download_data.yaml to have :

     logs:
   nasa_earth_data_logs_folder_path: "logs/download_data/20240711-081839/"

This will download the data based on your config/download_data.yaml configuration.
The current data sources that are supported are :

  • era5
  • nasa_earth_data
  • gov_can

Generate Dataset

Once the raw data has been downloaded, the data needs to be processed because some data might be daily, some might be bi-weekly or monthly and some data sources might return tiles while some might return one file for the entire Canada.
The goal of this step is to take all the data that was downloaded and produce as output tiles of the desired resolution and aggregated yearly.
Here is an overview of the various steps (at a high level) :

Note that this step outputs big tiles and these tiles are usually larger than the tiles that the model will take as input. This was done to ensure that we split our train/val data in a way that avoids leakage. Smaller tiles (eg: 128x128) will be created during training based on the big tiles.
Each big tile is of dimension C x H x W where = C the number of different sources of data (eg: NDVI, EVI, LAI, ...), H = 512 and W = 512.
So each big tile represents the data inputs stacked for 1 year for the area delimited by the 512x512 pixels area.

  1. Add the repo source code to the python path (make sure you are at the root of the repository) : 
    export PYTHONPATH=$(pwd)/src
  2. Edit the configuration (or leave as-is) under config/generate_dataset.yaml.
    • One can change the pixel size resolution (eg: 250 meters), tile size in pixels (eg: 512x512).
    • The sources names must match the folder name created during the download step.
  3. Execute the dataset generation script : 
    python generate_dataset.py

Resume from tmp folder

We can resume dataset generation by setting resume: true in the config and setting the resume_folder_path to a string representing the path to the dataset folder to resume (eg: "data/datasets/16f47c6b-fff0-424e-b5b5-b55ad6137cee").
The resume expects all data under the tmp folder of the resume_folder_path to have valid data that is done being processed.
For the dynamic input data, this means all data should be under resume_folder_path/tmp/year_1/input_data_1/tiles/ (for all years and all input data).
For the static input data it should be under resume_folder_path/tmp/static_data/input_data_1/tiles/.
The dataset generation will rebuild its index based on this assumption.
This means one can run multiple exeuction of the dataset generation script (eg: generate for year 1 then re-run the script for year 2) then combine the tmp folder content from both years to resume the dataset generation as if both years were computed in the same execution.
The resume logic resumes right before the stacking step in the dataset generation (which is then followed by the target generation).
Note: Make sure to set the option cleanup_tmp_folder_on_success: false to keep the tmp folder in order to resume from it.
Note 2 : If you do not wish to keep the temp data to resume the dataset generation, then you can set cleanup_tmp_folder_on_success: true, this will delete the temporary data if the dataset generation succeeds.

Split Dataset

  1. Add the repo source code to the python path (make sure you are at the root of the repository) :

    export PYTHONPATH=$(pwd)/src

  2. Set the PROJ_LIB environment variable to your conda environment :

    export PROJ_LIB="$CONDA_PREFIX/share/proj/"

  3. Update the config file config/split_dataset.yaml to specify where the data is :

    • Set data.input_data_periods_folders_paths to a list of string corresponding to the paths to the folder for the range of the input data (eg: '.../2023_2023').
      • This list is the list of all the periods (not just train).
    • Do the same for data.target_periods_folders_paths

  4. Run the train script :

    python split_dataset.py

Note : The script will generate a json file called data_split_info.json under the output directory of the data split. This file is used by the training script to load the data so take note of its location.

Data Quality

Currently, the default config is setup to have the following data quality checks :

  • min_percent_pixels_with_valid_data: 0.50
    • This will delete tiles from the training/validation/test set that do not contain at least 50% of its pixel as "valid pixel".
    • This was done to ensure the model does not fit noise (eg: tiles consisting of only noise or mainly noise).
    • This was also applied to validation and test set to ensure we compute metrics based on valid data only.
  • input_data_min_fraction_of_bands_with_valid_data: 0.5
    • This will treat any pixel that does not have at least 50% of its band with valid values as an invalid pixel.
    • Valid values means it is not nodata values.
  • max_no_fire_proportion: 0.0
    • This will delete from the training set (only), any tiles whos target consist of only 0s.
    • This helps combat the highly imbalanced nature of the data for wildfires.
    • The proportion = 0.0 means we do not allow any tile with only no fire pixels, 0.1 would mean we allow at most 10% of tiles with only 0s as target.
  • min_nb_pixels_with_fire_per_tile: 256
    • This will delete tiles from the training set (only), that do not have at least 256 pixels with a target = 1.
    • This helps combat the highly imbalanced nature of the data for wildfires.

Training

  1. Add the repo source code to the python path (make sure you are at the root of the repository) :

    export PYTHONPATH=$(pwd)/src

  2. Update the config file config/train.yaml to specify where the data_split_info file is :

    • Set data.split_info_file_path to a string representing the path of the json file.

  3. Setup the loggers if needed (see loggers)

  4. Run the train script :

    python train.py

Note : The script outputs training results under the output folder.

Loggers

The training config supports the following loggers:

Loguru

https://github.com/Delgan/loguru
No setup required, it will print to std.out.

Comet ML

https://www.comet.com/site/

  1. Set the following environment variables prior to running the training script : 
    export COMET_ML_API_KEY=<YOUR_API_KEY>
    export COMET_ML_PROJECT_NAME=<YOUR_PROJECT>
    export COMET_ML_WORKSPACE=<YOUR_WORKSPACE>

Predict

  1. Add the repo source code to the python path (make sure you are at the root of the repository) :

    export PYTHONPATH=$(pwd)/src

  2. Update the config file config/predict.yaml to specify where the data_split_info file is :

    • Set data.split_info_file_path to a string representing the path of the json file.

  3. Update the config file config/predict.yaml to specify where the input data is :

    • Set data.input_data_folder_path to a string representing the path to the input_data folder

  4. Run the predict script :

    python predict.py

Note : The script outputs 1 raster file which corresponds to the prediction map where each pixel has a probability of future wildfire occurrence assigned to it. You might want to add 0 as nodata value in QGIS when viewing the predicted map.

Deep Learning Model

  • The repository features a custom implementation of a U-Net Convolutional Neural Network.
  • Below is a graph of the UNet architecture coded in this repository which slightly deviates from the paper to produce an output that has the same size as the input image :

Contributing

  1. Follow the prerequisites steps.
  2. Download & Install Trufflehog Binary
  3. Install pre-commit hooks 
    pre-commit install --allow-missing-config
  4. Update .vscode/settings.json mypy.dmypyExecutable to point to the binary based on your conda env location (currently it assumes you use micromamba and have an environment named wildfire under /home/user/micromamba/envs/wildfire/)

About

Canada Wildfire Prediction Using Deep Learning

Topics

machine-learning deep-neural-networks deep-learning geospatial cnn geospatial-data semantic-segmentation ndvi wildfire modis-data modis-dataset era5 wildfire-detection canada-wildfire

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