HeatShield AI

Predict urban heat before it becomes a crisis, then automatically deploy the right resources where they are needed most.

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Inspiration

Extreme heat is often treated as a citywide weather problem, even though its effects can vary dramatically between neighbourhoods. One street may have tree cover, open spaces, and access to cooling, while another may be surrounded by concrete, traffic, crowded housing, and limited public infrastructure.

The people facing the greatest danger are often those who receive the least targeted support, including elderly residents, outdoor workers, children, people with medical conditions, and communities living in densely built areas.

Most existing heat-warning systems provide broad alerts such as “high temperature expected today.” These warnings are useful, but they do not answer the questions city authorities need answered:

  • Which neighbourhood will be most dangerous in the next few hours?
  • Which populations are most vulnerable?
  • Where should cooling centres, water stations, medical teams, and mobile misting units be deployed?
  • How can limited resources be allocated for the greatest possible impact?

We created HeatShield AI to move urban heat management from general warnings to predictive, street-level action.

What HeatShield AI does

HeatShield AI is an AI-powered urban heat intelligence and resource deployment platform.

The system combines environmental, satellite, geographic, demographic, and infrastructure data to generate a dynamic heat-risk map for each city zone. It predicts not only where temperatures will be highest, but where those temperatures are most likely to cause harm.

HeatShield AI then recommends how available resources should be deployed. These resources may include:

  • Mobile cooling units
  • Drinking-water stations
  • Ambulances and medical teams
  • Temporary shaded shelters
  • Misting fans
  • Public transport support
  • Alerts for outdoor workers and vulnerable residents
  • Extended operating hours for cooling centres

Instead of simply showing a red area on a map, the platform converts that prediction into an operational plan.

How we built it

The city is divided into small geographic cells using a spatial grid. For every cell, the platform collects or estimates features such as:

  • Air temperature and humidity
  • Land-surface temperature
  • Tree and vegetation coverage
  • Building density
  • Road and concrete coverage
  • Traffic intensity
  • Population density
  • Age and health vulnerability indicators
  • Distance from hospitals and cooling centres
  • Historical heat incidents
  • Weather forecasts
  • Availability of public resources

These inputs are processed through a machine-learning pipeline that predicts the expected heat risk for each zone over the next several hours.

The platform calculates a risk score using three main components:

[ R_i = H_i \times E_i \times V_i ]

where:

  • (H_i) represents the predicted heat hazard in zone (i)
  • (E_i) represents the number of people exposed
  • (V_i) represents the vulnerability of the exposed population

This allows the platform to distinguish between an unoccupied industrial zone with a high surface temperature and a crowded residential zone containing elderly residents and limited cooling access.

For the predictive model, we designed a hybrid architecture combining:

  • Gradient-boosted decision trees for structured environmental and demographic data
  • Time-series forecasting for short-term temperature and humidity prediction
  • Geospatial processing for neighbourhood-level risk mapping
  • Computer vision for extracting heat-related features from satellite imagery
  • An optimisation engine for resource allocation

The resource deployment engine treats city resources as limited assets. Its objective is to reduce the total population-weighted heat risk while respecting constraints such as resource capacity, travel time, operating cost, and availability.

A simplified optimisation objective is:

[ \min \sum_i R_i^{\text{remaining}} ]

subject to:

[ \sum_j x_{ij} \leq C_i ]

where (x_{ij}) represents the amount of resource (j) deployed to zone (i), and (C_i) represents operational constraints.

The system prioritises areas where a resource can produce the greatest reduction in risk. For example, it may recommend sending a mobile cooling van to a high-risk residential area, placing water stations near outdoor-worker zones, and keeping an ambulance close to an area with a high elderly population.

Platform workflow

  1. Environmental and city data are collected from weather APIs, sensors, satellite imagery, traffic feeds, and municipal datasets.
  2. The data are cleaned, aligned geographically, and converted into features.
  3. The AI model predicts heat conditions for each city zone.
  4. A vulnerability model estimates the likely human impact.
  5. The platform produces a live heat-risk map.
  6. The optimisation engine examines available city resources.
  7. It recommends deployment locations, routes, priorities, and estimated impact.
  8. City officials can approve, modify, or reject the recommendations through a control dashboard.
  9. New sensor readings and field reports continuously update the predictions.

Citizens can also use a mobile interface to view safer walking routes, nearby cooling centres, water stations, and local heat warnings.

What makes it different

Many heat-monitoring systems stop at prediction or visualisation. HeatShield AI connects four stages in one platform:

Predict → Prioritise → Deploy → Learn

The platform predicts dangerous heat, identifies the people most at risk, deploys limited resources, and measures whether those interventions reduced risk.

It is also designed as a decision-support system rather than a fully uncontrolled automation system. High-impact actions remain under human supervision, while routine recommendations and alerts can be automated.

What we learned

While developing HeatShield AI, we learned that temperature alone is not enough to measure urban heat danger. Heat risk depends on the interaction between weather, infrastructure, population exposure, medical vulnerability, and access to support.

We also learned that an accurate prediction has limited value unless it leads to an actionable decision. This changed our project from a heatmap generator into a complete urban response platform.

Another important lesson was the need for explainable AI. Municipal authorities must understand why a neighbourhood has been classified as high risk. For this reason, HeatShield AI displays the major factors influencing every prediction, such as low vegetation, high humidity, dense population, or long distance from a cooling centre.

Challenges we faced

One major challenge was combining data collected at different geographic and temporal scales. Satellite data may be updated daily, weather data may change hourly, and sensor readings may arrive every few minutes. We addressed this by converting all information into a common geographic grid and synchronised time intervals.

Another challenge was avoiding unfair resource allocation. Neighbourhoods with fewer sensors or incomplete historical records could otherwise appear less vulnerable. Our design therefore includes uncertainty estimates and demographic vulnerability indicators so that missing data do not automatically reduce an area’s priority.

Resource deployment also presented a difficult optimisation problem. The hottest zone is not always the zone where a resource will save the most people. The system must consider severity, population, vulnerability, distance, available capacity, and expected effectiveness at the same time.

Finally, we had to keep the project technically ambitious while ensuring that a realistic MVP could be developed. Our prototype focuses on a selected city region, uses publicly available datasets, generates neighbourhood heat-risk predictions, and demonstrates automated placement of a limited set of cooling resources.

Future development

Future versions of HeatShield AI could incorporate live hospital-admission data, wearable heat-stress signals, crowdsourced reports, IoT weather sensors, and feedback from emergency teams.

The platform could also simulate long-term interventions such as planting trees, installing cool roofs, changing road materials, and building permanent cooling centres. This would allow cities to compare immediate emergency responses with long-term infrastructure investments.

Our goal is to help cities act before extreme heat becomes a medical emergency. HeatShield AI does not simply show where a city is hot. It helps determine where people are in danger and what the city should do next.

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