XAI-tool4GEE

An explainable machine learning tool for land cover mapping and monitoring with GEE

Overview

• The explainable machine learning tool is a Jupyter notebook that can be run directly on Google Colaboratory (Google Colab), which requires no setup on local computers and runs entirely in a browser by remotely connecting with Google's cloud servers.
• The core functionality of the notebook is built mainly upon two Python packages geemap and ipywidgets.
• geemap is a Python package for interactive mapping with GEE, which uses the Python API to make computational requests to the Earth Engine servers. Empowered by ipyleaflet and ipywidgets, geemap allows users to interactively analyze and visualize the Earth Engine datasets with Jupyter notebooks.
• The scikit-learn and shap packages are also used to calculate the feature importance values.
• The Colab’s layout widgets are used to organize the classification results and feature importance plots into different display tabs.

User interface

Workflow for LULC mapping

The typical steps for performing a land cover classification consists of

• determining the study area,
• selecting the data source (satellite sensors/bands) and the range of dates to extract the composite image to be classified,
• preparing sufficient labeled data for supervised classification,
• selecting a classifier with default or custom parameters,
• classifying the image,
• and performing accuracy assessments and some post-processing visualizations.

Reference

Chen, H.; Yang, L.; Wu, Q. Enhancing Land Cover Mapping and Monitoring: An Interactive and Explainable Machine Learning Approach Using Google Earth Engine. Remote Sens. 2023, 15, 4585. https://doi.org/10.3390/rs15184585

Examples of how-to use

Example notebook output

Upload_Google_samples.mp4

Example notebook output

Sample_ESRI_landcover.mp4

Example notebook output

Dubai_land_reclaimation.mp4