Land Usage Classification in Agriculture

Abstract

This repository explores the classification of land use types using aerial and satellite photographs of the earth's surface. The project focuses on creating photo masks based on ready-made masks and visualizing them on the screen.

Introduction

Determining how land is used is a significant challenge with implications for both economic and natural disasters due to improper and illegal land use. This project addresses this issue by leveraging the analysis of aerial and satellite images. The key goal is to build a mathematical model capable of determining land use types based on color analysis. By utilizing artificial intelligence and big data methods, the project aims to develop a robust model-classifier.

Materials and Methods

The statistical data used in this project was obtained from Kaggle under the CC0: Public Domain license.

Reference

• Yaroslav Vyklyuk, Prof., PhD., DrSc

Getting Started

To replicate or experiment with this project, follow the steps outlined below.

Prerequisites

• Python
• Jupyter Notebooks
• Required libraries (matplotlib, scikit-learn, opencv-python, colormap, skillsnetwork, etc.)

Installation

pip install -r requirements.txt

Data Preparation

Run the following command to download the necessary dataset:

python prepare_data.py

Usage

1. Visualize and explore the provided Jupyter notebooks.
2. Use the functions and scripts to analyze and classify land usage based on images.
3. Experiment with your own datasets and models.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• The dataset used in this project is credited to Kaggle.