Assessing Urban Heat Island Mitigation through Multi-Temporal Remote Sensing and 3D GIS Modelling for Smart City Planning

Project Abstract

Assessing urban heat island (UHI) mitigation through an integrated framework that combines multi-temporal remote sensing, high-resolution 3D GIS modeling, and urban climate analytics to inform smart city planning, this study investigates the spatiotemporal dynamics of urban heat accumulation and identifies effective intervention strategies. The research employs a multi-sensor satellite data suite including Landsat, Sentinel-2, and Sentinel-5P, spanning a decade to capture seasonal and interannual variability in surface and near-surface temperatures, land surface albedo, vegetation indices, and anthropogenic heat emissions. By fusing these datasets with terrestrial LiDAR and high-resolution UAV-derived 3D city models, the study constructs an enhanced 3D urban morphology framework that links vertical zoning, roof geometry, albedo, material properties, and built-environment heat retention to thermal exposure patterns. Advanced algorithms for radiative transfer, energy balance modeling, and machine learning-based downscaling are applied to derive accurate near-surface air temperature distributions at fine spatial (?10 m) and temporal (daily to hourly) scales. The methodological novelty lies in the seamless integration of temporal remote sensing observables with a physics-informed 3D city model to simulate UHI phenomena under various mitigation scenarios, including reflective roofing, green and blue infrastructure, porous pavements, and shading through urban canopy management. The research designs a decision-support toolkit that translates complex thermal metrics into actionable planning indicators for policymakers, urban designers, and utility providers. Key objectives include quantifying the contribution of land-use/land-cover changes, building density, and surface materials to UHI intensity; evaluating the thermal relief potential of green roofs and vertical gardens; assessing the cooling benefits of shade trees and water bodies in diverse microclimates; and optimizing the spatial allocation of cooling interventions to maximize temperature reductions at the urban scale. Through cross-validation with in-situ meteorological stations and mobile transect measurements, the study ensures robust accuracy of thermal estimations and model outputs. The anticipated outcomes include (1) a high-fidelity temporal map of UHI indices across the study city, (2) a probabilistic assessment of intervention effectiveness under climate change scenarios, (3) a scalable workflow for other urban contexts, and (4) a stakeholder-oriented dashboard featuring scenario comparison, cost-benefit analysis, and policy recommendations. The research contributes to the literature on urban climate adaptation by bridging remote sensing, 3D city modeling, and smart planning frameworks, enabling evidence-based strategies to mitigate heat stress, reduce energy demand, and enhance urban livability. The implications extend to zoning regulations, building codes, and municipal climate action plans, providing a replicable blueprint for cities aiming to integrate climate resilience with sustainable urban development.

Project Overview

What This Project Is About

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The project looks at how cities heat up (urban heat island) and how to cool them using two main tools: taking repeated satellite pictures over time and building 3D maps of the city to plan better. It combines simple observations of land surfaces, buildings, and green spaces to explore how different features affect temperatures and how to design cooler urban areas.

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The Problem It Addresses

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Many cities feel hotter than nearby rural areas, which can worsen health, energy use, and comfort. Traditional maps don’t show the three-dimensional layout or how temperature changes day by day. This project fills that gap by linking temperature data with 3D city models to identify where heat is trapped and how to fix it.

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Objectives of the Project

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1. Understand how urban surfaces and building shapes influence heat buildup.
2. Use multi-temporal imagery to track temperature changes over seasons and years.
3. Build a simple 3D model of a city area to visualize heat distribution.
4. Suggest practical design or policy ideas to reduce heat (e.g., more trees, reflective surfaces).

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What You Will Do Step by Step

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1. Learn basic, non-technical terms for heat and city design.
2. Collect short-term and long-term temperature data from accessible satellite sources.
3. Create a simple 3D city model showing buildings, streets, and green spaces.
4. Analyze how different features relate to higher or lower temperatures.
5. Identify hotspot areas and test low-cost cooling ideas using the model.

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Expected Outcome

A clear set of findings on what urban features contribute most to heat, a simple 3D visualization of the city’s heat hotspots, and practical recommendations for planners and communities to reduce urban heat effectively.