ai4FastEvolution

An official implement of Uncovering the Hidden Consequences of Rapid Adaptation in Invasive Plants via a Deep Learning Approach

Environment Setup

To set up the required environment, you can choose from the following options:

• Using pip: You can install the necessary Python dependencies from the requirements.txt file using the following command:

  pip install -r requirements.txt
  

We highly recommend using Docker to set up the required environment. Two Docker images are available for your convenience:

• Using Docker from ali cloud:

  • registry.cn-hangzhou.aliyuncs.com/cyd_dl/monai-vit:v26

  docker pull registry.cn-hangzhou.aliyuncs.com/cyd_dl/monai-vit:v26
  

• Using docker from dockerhub:

  • cyd_docker:v1

  docker pull ydchen0806/cyd_docker:v1
  

Contents

• /code - Contains all runnable code.

• /raw_data - Contains original UAV imagery data and weather station data.

• /weather_data_1005 - Contains two climate datasets:

  • ERA5-Land hourly data - Hourly surface reanalysis data.
  • Precipitation monthly and daily gridded data - Monthly and daily gridded precipitation data.
  • China weather station data. - Weather data from the Chinese government.

Code

Here is a list of the code files in this repository:

• data_preprocess_more_data.py - Generates the initial dataset.
• get_weather_data.py - Incorporates weather data.
• get_growth_area.py - Calculates lesion distances.
• data_pca.py - Reduces dimensionality via PCA.
• MGWR.py - Fits a geographically weighted regression model to estimate beta.(Update soon)
• auto_ml_fit.py - Fits lesion growth rates using machine learning.
• get_cluster.py - Performs clustering on the final results.

Workflow Overview

The overall workflow can be summarized as follows:

1. data_preprocess_more_data.py generates the initial dataset.
2. get_weather_data.py incorporates weather data.
3. get_growth_area.py calculates lesion distances.
4. data_pca.py reduces dimensionality via PCA.
5. Use MGWR to fit a geographically weighted regression to estimate beta.
6. auto_ml_fit.py fits lesion growth rates using machine learning.
7. get_cluster.py clusters the final results.

This workflow provides a high-level overview of the steps involved in your codebase.