HeatSense

An AI-powered web application for analyzing urban heat islands using satellite data, land use classification, and weather measurements to identify heat hotspots and generate evidence-based recommendations for sustainable urban planning

Overview

A comprehensive tool for analyzing urban heat island effects using satellite imagery, weather station data, and land use information. Built for Berlin suburb analysis.

Features

• 🛰️ Satellite Analysis: Landsat thermal band processing via Google Earth Engine
• 🌡️ Weather Validation: DWD weather station data integration for ground truth validation
• 🏙️ Land Use Correlation: CORINE land cover data correlation analysis
• 📊 Spatial Analysis: Advanced heat island hotspot identification and clustering
• 🌐 Web Interface: Modern Flask-based web application
• ⚡ Multiple Performance Modes: From quick preview to detailed analysis
• 📱 Interactive Maps: Real-time visualization with Leaflet maps

Quick Start

Prerequisites

• Python 3.11+
• Google Earth Engine account
• uv package manager (recommended)

Installation with uv (Recommended)

# Clone the repository
git clone https://github.com/your-username/HeatSense.git
cd HeatSense

# Install dependencies with uv
uv sync

# Set up Google Earth Engine authentication
uv run python -c "import ee; ee.Authenticate()"

# Copy environment configuration
cp .env.example .env
# Edit .env and add your Google Earth Engine project ID

Alternative Installation with pip

# Clone the repository
git clone https://github.com/your-username/HeatSense.git
cd HeatSense

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install package in development mode
pip install -e .

# Set up Google Earth Engine authentication
python -c "import ee; ee.Authenticate()"

# Copy environment configuration
cp .env.example .env

Launch Web Application

# With uv (recommended)
uv run python run_webapp.py

# Or with pip installation
python run_webapp.py

Open your browser to http://localhost:8000 🎉

Command Line Analysis

# Quick analysis with uv
uv run python run_analysis.py --area "Friedrichshain-Kreuzberg" --start-date 2023-07-01 --end-date 2023-07-31

# Detailed analysis with output file
uv run python run_analysis.py --area "Berlin" --start-date 2023-06-01 --end-date 2023-08-31 --mode detailed --output results.json

# With verbose logging
uv run python run_analysis.py --area "Mitte" --start-date 2023-07-15 --end-date 2023-07-20 --mode preview --verbose

Programmatic Usage

from src.heatsense.webapp.analysis_backend import UHIAnalysisBackend

# Initialize backend
backend = UHIAnalysisBackend()

# Run analysis
result = backend.analyze(
    area="Friedrichshain-Kreuzberg",
    start_date="2023-07-01",
    end_date="2023-07-31",
    performance_mode="standard"
)

# Access results
print(f"Mean temperature: {result['data']['summary']['temperature_overview']['mean']}°C")
print(f"Hotspots found: {result['data']['summary']['hotspots_count']}")

Performance Modes

Mode	Speed	Resolution	Weather Data	Use Case
Preview	<30s	300m	❌	Quick exploration
Fast	30-60s	200m	❌	General analysis
Standard	1-3 min	100m	✅	Balanced quality
Detailed	3-10 min	50m	✅	Research & planning

Project Structure

HeatSense/
├── run_webapp.py          # 🎯 Launch web application
├── run_analysis.py        # 🎯 CLI analysis tool
├── pyproject.toml         # Python project configuration
├── .env.example           # Environment variables template
├── src/
│   └── heatsense/         # Main package
│       ├── config/        # Configuration files
│       ├── data/          # Data processing modules
│       ├── utils/         # Utility functions
│       └── webapp/        # Web application
├── docs/                  # Documentation
├── examples/              # Usage examples
└── temp/                  # Temporary analysis files

Data Sources

• 🛰️ Satellite Data: Landsat 8 thermal imagery (Google Earth Engine)
• 🌡️ Weather Data: DWD (German Weather Service) station network
• 🏙️ Land Use Data: CORINE Land Cover (European Environment Agency)
• 🗺️ Administrative Boundaries: Berlin WFS services

Examples

Check out the examples/ directory:

# Run basic analysis example
uv run python examples/basic_analysis.py

Documentation

Detailed documentation in the docs/ directory:

• UrbanHeatIslandAnalyzer.md - Core analysis engine
• DWDDataDownloader.md - Weather data integration
• CorineDataDownloader.md - Land use processing
• WFSDataDownloader.md - Boundary data access

License

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

Acknowledgments

• Google Earth Engine for satellite data access
• DWD (Deutscher Wetterdienst) for weather station data
• European Environment Agency for CORINE land cover data
• Berlin Senate for administrative boundary data

Issues & Support

Found a bug or need help? Please open an issue on GitHub.

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