Beyond the Forecast: Building HeatShield Chennai for Street-Level Heat Risk

Imagine if your route could think beyond distance and account for the heat you face. HeatShield Chennai transforms weather data into hyperlocal heat-risk insights by combining AWS-powered analytics, environmental factors, and heat-aware navigation to help people make more informed travel decisions. By connecting street-level risk estimation with personalised guidance and cloud-based alerts, it reimagines how technology can help Chennai navigate a hotter future.

Balaji M

Published Oct 11, 2026

Last modified Oct 11, 2026

Chennai Is Heating Up. What If Your Route Could Think About Heat?
The shortest way home may not be the route that makes the most sense on a hot Chennai afternoon. Two streets might share almost the exact same distance, yet offer very different exposure to heat.
While most navigation apps help us find the quickest way from point A to point B, distance is only part of the equation on a scorching Chennai afternoon. The route itself matters. HeatShield Chennai explores a different question: what if navigation could consider not just how far you walk, but also the heat risk along the way?
Built on AWS, HeatShield combines weather data, local environmental factors, heat-risk estimation, and heat-aware routing to help people explore their surroundings with heat in mind.
From City-Wide Forecasts to Local Heat Risk
A city-wide forecast provides useful information, but it cannot explain how conditions differ from one street to another.
Instead of treating the entire city as if microclimate conditions were uniform, HeatShield Chennai focuses on a dense 5 km × 5 km high-exposure urban corridor across South-Central Chennai. Encompassing key commercial, transit, and residential hubs—including T. Nagar, Saidapet, Nandanam, Guindy, and Teynampet—this corridor is divided into 100 micro-cells of approximately 500 m × 500 m each to create a truly localized view of heat risk.
• Data Retrieval: The application retrieves hourly temperature, relative humidity, and apparent temperature data from Open-Meteo.
• Processing: An hourly AWS Lambda process refreshes the weather inputs and recalculates cell-level risk estimates.
• Fallback: If live weather data is unavailable, the system falls back to an offline climate model.
The result is a map designed to help users explore how estimated heat risk varies across the selected area.
  2.Making Heat Risk Understandable—and Routes More Useful
A coloured map is only useful if people can understand what it represents.
HeatShield combines weather conditions with factors such as greenery, built-up density, time of day, and sensor anomalies to estimate risk. Its model produces a score from 0 to 100, grouped into green, amber, and red risk bands. The aim is not to label a location as definitively safe or dangerous, but to provide an understandable estimate that supports better-informed decisions.
The project also explores a different approach to navigation by recognizing that the shortest route may not always be the preferred route. Using a directed walking graph and Dijkstra's algorithm, HeatShield offers two route options:
• Fast route: Prioritises walking distance.
• Cool route: Adjusts route costs according to estimated heat risk and selected cooling-related points of interest.
This allows users to compare a distance-focused journey with a route that gives greater consideration to heat exposure. The route is a model-based recommendation, not a guarantee of lower real-world body temperature.
   3.Different People, Different Needs
Heat does not affect everyone in the same way. Outdoor workers, children, and older adults have different levels of vulnerability and unique needs.
To address this, HeatShield includes profile-based guidance to explore how recommendations vary by user profile. It also provides an interactive map, a time-of-day timeline, English and Tamil language support, and voice-related accessibility features.
For municipal users, a dashboard offers another perspective. Instead of viewing a single journey, they can examine estimated risk across multiple cells and identify locations that may deserve closer attention. The broader idea is to connect individual decisions with a view that could support local planning—without presenting model estimates as official public-health warnings
  4.How AWS Powers HeatShield Chennai
AWS provides the cloud infrastructure connecting the application's data, calculations, and user-facing experience through the following components:
• Amazon S3 & Amazon CloudFront: Host and deliver the frontend and spatial assets.
• Amazon API Gateway & AWS Lambda: Handle API requests and application logic.
• Amazon DynamoDB: Stores sensor readings and calculated cell scores, utilizing a time-to-live (TTL) policy for expiring suitable records.
• Amazon EventBridge Scheduler: Triggers the hourly refresh workflow.
• Amazon Bedrock: Supports contextual, bilingual heat-safety guidance, with a fixed-template fallback.
• Amazon SNS: Delivers heat-risk alerts when the configured score threshold is reached.
• AWS SAM: Supports infrastructure definition and deployment.
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   5.From Simulation to a Working Cloud Workflow
A heat-aware system requires much more than a static map. It needs a dynamic framework to refresh environmental conditions, process incoming data, update risk estimates, and respond immediately when configured alert thresholds are breached.
HeatShield’s cloud workflow integrates these moving parts seamlessly:
• Scheduled Automation: An Amazon EventBridge Scheduler triggers the hourly data refresh.
• Data Processing: Weather inputs are processed efficiently via AWS Lambda.
• Storage: Updated cell scores are stored securely within Amazon DynamoDB.
• Alerts & Guidance: Amazon SNS has been tested to deliver immediate alerts when risk thresholds are reached, while Amazon Bedrock provides contextual, profile-based guidance (with a template-based fallback system available if needed).
To evaluate how fluctuating local readings impact the system without relying on physical hardware during development, a Python sensor simulator was built to broadcast sample telemetry through Amazon API Gateway. A Lambda handler captures these simulated readings and stores them in DynamoDB for the risk-calculation workflow to use.
The resulting deployed prototype successfully unifies live weather inputs, scheduled processing, automated risk estimation, interactive routing, and cloud-based alerts. Every component works toward a singular goal: making local heat risk easier to explore and comprehend.
Taking Heat-Aware Navigation Further
HeatShield serves as a foundational starting point for making urban heat data actionable. The next phase focuses on strengthening the system’s local precision and extending its real-world utility by:
• Integrating physical sensors on the ground.
• Comparing model-estimated risk against observed local conditions.
• Refining environmental data inputs.
• Evaluating route recommendations through clearly defined testing scenarios.
As empirical evidence improves, HeatShield can evolve from a functional prototype into a thoroughly validated tool for both local residents and urban planners. Throughout this evolution, the focus remains on system transparency, ensuring users can freely explore estimates, compare routes, and recognize exactly what the available data can—and cannot—tell them.
A Different Way to Think About Urban Heat
Heat is never experienced as a single, uniform number on a city forecast. It is felt while walking to work, waiting outdoors, traveling through a neighborhood, or running essential daily errands.
HeatShield Chennai demonstrates how cloud-powered geospatial analysis can transform standard weather statistics into a highly localized, understandable view of heat risk—proving that modern navigation can consider far more than just distance. The goal is not simply to plot heat on a map, but to make local heat risk easier to see, question, and act on.
Explore the live demo:  https://d2lmxv59vmxpg4.cloudfront.net/ 