Urban Heat Island Detection & Cooling Infrastructure Assessment

Project Summary

Urban Heat Islands (UHI) significantly impact urban livability, energy consumption, and public health.
This project implements an end-to-end automated geospatial pipeline to detect UHI hotspots and evaluate cooling infrastructure using satellite-derived Land Surface Temperature (LST) and OpenStreetMap spatial features.

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Technical Objectives

• Automate LST extraction and preprocessing from Landsat 9
• Perform spatial zonal statistics on cooling infrastructure
• Classify urban heat zones using unsupervised machine learning (K-Means)
• Produce reproducible geospatial outputs for GIS workflows

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Data Sources

Dataset	Source
Land Surface Temperature (LST)	Landsat 9 via Google Earth Engine
Green spaces & Water bodies	OpenStreetMap (Overpass API)
Administrative Boundary	Pune City GeoJSON

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Technology Stack

Category	Tools / Libraries
Remote Sensing	Google Earth Engine, Rasterio
Spatial Analysis	GeoPandas, RasterStats, Shapely
Machine Learning	Scikit-learn (K-Means clustering)
Data Handling	Pandas, NumPy
Visualization	Matplotlib
Automation	Python scripting pipeline
Environment	Google Colab / Python

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Pipeline Architecture

Phase 1 – Study Area & Feature Extraction

• Defined Pune city boundary using GeoJSON
• Extracted green spaces and water bodies using Overpass API

Phase 2 – Land Surface Temperature Processing

• Retrieved Landsat 9 LST data from Google Earth Engine
• Cleaned raster, masked invalid values, and clipped to city boundary

Phase 3 – Cooling Feature Zonal Statistics

• Computed mean LST for each green and water feature
• Enriched vector datasets with temperature metrics

Phase 4 – Urban Heat Island Classification

• Applied K-Means clustering on cleaned LST raster
• Classified the city into thermal zones (cool → extreme heat)

Phase 5 – Visualization & Export

• Generated heatmaps and spatial overlays
• Exported GeoTIFF and GeoJSON for GIS integration

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Repository Structure

/Urban_Heat_Island_Detection
├── uhi.py                           # Main Python script
├── UHID.md                          # Project documentation
├── UHI
│   ├── Raw/
│   │   ├── pune_boundary.geojson    # Study area boundary
│   ├── Outputs/
│   │   ├── pune_green_water_fulltags.geojson   # Green & water features
│   │   ├── pune_cooling_with_LST.geojson       # Features enriched with LST
│   │   ├── LST.tif                            # Original LST raster
│   │   ├── LST_cleaned.tif                    # Preprocessed/masked raster
│   │   └── kmeans_uhi_clusters.tif            # Classified UHI zones
│   └── PNG/
│       ├── masked_LST_cleaned.png             # Cleaned LST preview
│       └── phase4_kmeans_clusters.png         # UHI clustering visualization

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Key Outputs

File	Description
kmeans_uhi_clusters.tif	Classified UHI thermal zones raster
pune_cooling_with_LST.geojson	Cooling features enriched with zonal mean temperature
masked_LST_cleaned.png	Cleaned LST visualization
phase4_kmeans_clusters.png	UHI clustering heatmap

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How to Run

# Clone repository
git clone https://github.com/prachisarode95/Urban-Heat-Island-Detection-Pipeline
cd Urban-Heat-Island-Detection-Pipeline

# Install dependencies
pip install -r requirements.txt

# Run automation pipeline
python scripts/uhi.py

Requirements.txt

geemap
geopandas
rasterio
numpy
pandas
scikit-learn
rasterstats
matplotlib
shapely

Key Outcomes

File	Description
kmeans_uhi_clusters.tif	Classified UHI thermal zones raster
pune_cooling_with_LST.geojson	Cooling features enriched with zonal mean temperature
masked_LST_cleaned.png	Cleaned LST visualization
phase4_kmeans_clusters.png	UHI clustering heatmap