Spatial Resolution Enhancement of Daily Fire Maps using MODIS Active Fire Data

Colab File

How to plot Modis and VIIRS grid? Download specific years CSV file from following urls:Firms's website[https://firms.modaps.eosdis.nasa.gov/download/]. Use the dataset to plot grids through the colab file.

File description

The brief description for each file is shown in the table below:

File description	File description
File name Description modis_downloader.py download MODIS LST image from NASA webpage (more information below) modis_data_preprocessing.py preprocess the downloaded MODIS images utils.py contain all helper functions tiff_process.py preprocess the tif files and transform them into npy arrays	File name Description model.py definition for Multi-residual U-Net model dataset.py create Pytorch dataset object run_inference.py run the trained network train.py train the network

0. Requirements

pip install -r requirements.txt

1. Data downloading and database preparation

User should firstly register on NASA's website[https://urs.earthdata.nasa.gov/users/new]. User could use the following command to download automatically MODIS dataset with the user name and corresponding password, the downloaded data will be placed folder "MODIS" in current directory.

python modis_downloader.py --username <username> --password <password> 

After downloading the raw data for the desired years, data need to be pre-processed, which involves cropping, cloud and sea pixels elimination and downsampling:

python modis_data_preprocessing.py 

2. Train and test Multi-residual U-Net

Our principle contribution in this project is to design and implement a new deep learning model based on U-net architecture called Multi-residual U-Net. The architecture of this model is shown as below:

Train

To train the network with your tif data, please make sure to put all of the data into a folder and run the following command:

python train.py --datapath <path/to/training/tifs/directory> --model_name <name of the model> --lr <learning rate> --epochs <number of epochs> --batch_size <size of batch> --continue_train <True/False>

P/s: The checkpoint of training process lies in the same directory as the train.py.

3. Result