This repository contains the source code enclosed to the research article titled "A novel cloud-oriented procedure based on SAR and Multispectral data to detect burnt areas". The following instructions will help you to download the code, setup your environment and execute the workflow.

Code download

Clone the content of this reporitory in a local folder (e.g. using git clone)

Environment setup

This project has been tested and run in a Linux Ubuntu environment.

Step 1

• Create the folder data inside your local folder.

• Inside the file .env, set the value for LOCAL_DATA_PATH to point to the path of the folder data you created (e.g. LOCAL_DATA_PATH=./data)

• Create the following folders inside the folder data:

  • s1-pre
  • s1-post
  • s2-pre
  • s2-post
  • corine

• Copy into the folders created in the previous step the four input Sentinel images to be processed by the workflow in the form of the zipped products. For example:

  • S1A_IW_GRDH_1SDV_20210804T062638_20210804T062703_039076_049C51_D70E.zip inside the folder s1-pre
  • S1A_IW_GRDH_1SDV_20210829T181128_20210829T181153_039448_04A911_FED5.zip inside the folder s1-post
  • S2A_MSIL2A_20210809T110621_N0301_R137_T30TUK_20210809T143014.zip inside the folder s2-pre
  • S2A_MSIL2A_20210819T110621_N0301_R137_T30TUK_20210824T112618.zip inside the folder s2-post
  • U2018_CLC2018_V2020_20u1.tif inside the folder corine

Step 2

Automatic setup using Docker

• Install Docker Desktop

• Run the following command to create a Docker container, which will download and install all dependencies: sudo docker compose up -d --build --force-recreate

Workflow execution

• Open the following url in a web browser 'http://localhost:8080' (use the following credentials "airflow:airflow" or "admin:airflow")

• Run the 's1_s2_fire_detection' DAG, wait for completion and then access the produced results