Urban-Heat-Island-Mapping-using-AI

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Urban-Heat-Island-Mapping-using-AI

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Urban-Heat-Island-Mapping-using-AI is a machine learning project that analyzes and predicts urban temperature variations using synthetic environmental data. It identifies heat hotspots influenced by vegetation, population density, and infrastructure.

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🌍 Urban-Heat-Island-Mapping-using-AI
📌 Project Overview

Urban Heat Island (UHI) is a phenomenon where urban areas experience significantly higher temperatures than surrounding rural regions due to human activities, infrastructure, and reduced vegetation.

This project leverages Artificial Intelligence (AI) and synthetic environmental data to map and analyze Urban Heat Island effects. The goal is to demonstrate how machine learning models can predict temperature variations and identify heat hotspots within urban environments.

🎯 Objectives

Analyze temperature variations across urban regions

Identify heat hotspots using AI models

Simulate environmental factors influencing UHI

Visualize spatial heat distribution

Support climate-resilient urban planning decisions

🧠 Technologies Used

Python

NumPy

Pandas

Matplotlib / Seaborn

Scikit-learn

Synthetic Dataset Generation

📊 Dataset

This project uses synthetic data representing:

Surface temperature

Vegetation index

Population density

Building density

Humidity levels

Geographic coordinates

Synthetic data is generated to simulate real-world urban environmental conditions.

⚙️ Methodology

Generate synthetic environmental data

Preprocess and normalize features

Train a machine learning model (e.g., Random Forest / Linear Regression)

Predict urban temperature distribution

Visualize heat maps and hotspot zones

📈 Expected Output

Temperature prediction model

Heatmap visualization of urban hotspots

Feature importance analysis

Model performance metrics (R², MAE, RMSE)

🚀 How to Run
git clone https://github.com/your-username/Urban-Heat-Island-Mapping-using-AI.git
cd Urban-Heat-Island-Mapping-using-AI
pip install -r requirements.txt
python main.py
🏙️ Applications

Smart city planning

Climate adaptation strategies

Environmental monitoring

Sustainable infrastructure development

🔮 Future Improvements

Integration with satellite imagery

Real-time temperature monitoring

Deep learning-based spatial modeling

Web-based interactive dashboard

🤝 Contribution

Contributions, issues, and feature requests are welcome!

📜 License

This project is open-source and available under the MIT License.

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