Mastering Machine Learning based Land Use Classification with Python: A Comprehensive Guide

Blog Link: https://medium.com/@waleedgeo/lulc-py-78cb954673d

This repository contains the code and data used for the blog post "Mastering Machine Learning based Land Use Classification with Python: A Comprehensive Guide".

In this blog post, we explore the world of machine learning based land use classification using Python. We take a deep dive into advanced techniques and algorithms for accurate land use classification. Whether you are a beginner or an experienced data scientist, this comprehensive guide will equip you with the tools and knowledge needed to master land use classification and transform the way you analyze and manage land resources.

Contents

• The materials directory contains the input rasters (rasters) and labels (shapefiles) used in this blog.
• lulc_py.ipynb Jupyter Notebook is the main working file for the land use classification. It contains the code for the land use classification using machine learning algorithms.

Requirements

• Python 3.6 or later
• Required Python packages: pandas, numpy, scikit-learn, matplotlib, seaborn, rasterio

Usage

1. Clone the repository using git clone https://github.com/waleedgeo/lulc_py.git
2. Navigate to the code directory and run the lulc_py.ipynb Jupyter Notebook using any IDE of your choice. The notebook contains the code for land use classification using machine learning algorithms.
3. The results of the land use classification will be saved in the materials/results directory.

Contributions

Contributions to this repository are welcome. If you find any bugs or have suggestions for improvements, feel free to submit an issue or pull request.

land-cover-classification