https://github.com/azad77/lulc_cnn_eurosat
Science Score: 13.0%
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Low similarity (10.5%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: Azad77
- Language: Jupyter Notebook
- Default Branch: main
- Size: 508 KB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
LULCCNNEuroSAT
This repository contains code for implementing a Convolutional Neural Network (CNN) for land use and land cover classification using the EuroSAT dataset and PyTorch. The EuroSAT dataset is based on Sentinel-2 satellite images and includes 10 classes representing various land use and land cover types.
Project Overview
This project demonstrates how to use a CNN for image classification tasks with the EuroSAT dataset. The implementation includes: - Data preprocessing and augmentation - Building and training a CNN model - Evaluating model performance - Making predictions on sample images
The CNN model used is based on the ResNet-50 architecture, and the code covers the full workflow from data loading to model evaluation.
Prerequisites
Before running the code, ensure you have the following libraries installed:
- torch and torchvision for deep learning
- PIL for image processing
- matplotlib, seaborn, and pandas for data visualization and manipulation
- scikit-learn for evaluation metrics
You can install these libraries using pip:
bash
pip install torch torchvision pillow matplotlib seaborn pandas scikit-learn
Installation
Clone this repository to your local machine:
bash
git clone https://github.com/yourusername/LULC_CNN_EuroSAT.git
Navigate to the project directory:
bash
cd LULC_CNN_EuroSAT
Usage Download and Unzip the EuroSAT Dataset:
Download the EuroSAT dataset and unzip it into the ./EuroSAT/ directory. You can use the following commands:
bash
wget http://madm.dfki.de/files/sentinel/EuroSAT.zip -O EuroSAT.zip
unzip -q EuroSAT.zip -d 'EuroSAT/'
rm EuroSAT.zip
References:
Reid Falconer, Land Use and Land Cover Classification (Beating the Benchmark). Available at: GitHub Repository
Helber, P., Bischke, B., Dengel, A., & Borth, D. (2018). Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. arXiv preprint arXiv:1709.00029. Available at: arXiv
Ankur Mahesh & Isabelle Tingzon, Land Use and Land Cover Classification using PyTorch. Available at: Google Colab
Owner
- Name: Dr Azad Rasul
- Login: Azad77
- Kind: user
- Company: Soran University
- Repositories: 4
- Profile: https://github.com/Azad77
As a geographer, I use remote sensing and GIS methods and techniques to study LST, urban environment, earth observation and natural disasters.
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