LU-LC-classification

Land-Use/ Land use classification in GEE

Land-Use / Land-Cover Classification in Google Earth Engine (GEE)

This project demonstrates a comprehensive approach to land-use and land-cover (LULC) classification using the powerful capabilities of Google Earth Engine. By leveraging satellite imagery, machine learning algorithms, and cloud-based processing, we aim to accurately map and analyze land-use patterns over time.

Table of Contents

• About
• Key Features
• Data Sources
• Methodology
• Results
• Usage
• Contributing
• License

About

LULC classification is essential for understanding environmental changes, urban development, agricultural patterns, and resource management. This project provides a streamlined workflow to classify land cover into categories such as:

• Urban Areas
• Forests
• Agriculture
• Water Bodies
• Barren Land
• …and more

Key Features

• Cloud-Based Processing: Utilizes Google Earth Engine’s scalable infrastructure for efficient analysis of large-scale geospatial data.
• Machine Learning Integration: Employs machine learning algorithms (e.g., Random Forest, SVM, deep learning) for accurate classification.
• Multi-Temporal Analysis: Enables the analysis of land-use changes over time by utilizing satellite imagery from different periods.
• Customizable: Easily adaptable to specific regions, timeframes, and classification schemes.

Data Sources

• Sentinel-2: High-resolution multispectral imagery for detailed land-cover mapping.
• Landsat: Long-term archive of satellite imagery for historical analysis.
• Other: Potentially incorporates additional datasets like MODIS, DEM, and auxiliary data (e.g., climate, soil).

Methodology

1. Data Preprocessing:
   • Cloud masking, atmospheric correction, and image normalization.
   • Selection of optimal spectral bands and indices (e.g., NDVI, EVI).
2. Training Data Collection:
   • Careful selection of representative training samples for each land-use class.
   • Can utilize existing LULC products or create custom samples using visual interpretation or field surveys.
3. Classification:
   • Training and validation of machine learning models.
   • Evaluation of model performance using accuracy assessment metrics.
4. Post-Processing:
   • Smoothing and filtering to refine classification results.
   • Accuracy assessment and validation using ground truth data (if available).

Results

Showcase your classification results here:

• Maps: Visualizations of classified land-cover maps for different time periods.
• Statistics: Area estimates and change detection analysis for each land-use category.
• Accuracy Assessment: Report the accuracy metrics of the classification model.

Usage

Provide clear instructions on how to use the code in this repository:

1. Environment Setup: Install Google Earth Engine Python API and other necessary libraries.
2. Data Acquisition: Specify your region of interest and time period.
3. Model Training: Select a machine learning algorithm and train it on your training data.
4. Classification and Analysis: Run the classification script and explore the results.

Contributing

We welcome contributions! Please see our CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License.