World heat map

Hyperlocal mapping and simulation of urban heat islands using AI

The KLIPS project in Dresden demonstrates how AI and real-time environmental data can support climate adaptation in cities. Using over 250 temperature sensors and satellite imagery, the project maps and forecasts urban heat islands to inform data-driven, climate-resilient urban planning.

Event details

artificial intelligence climate protection & adaptation data visualisation planning & modelling
Datetime
27.10.2025, 13:00 - 13:30
Event type
Online (virtual)
Dokumentation

Paragraphs

Key takeaways

  • AI for climate adaptation: Machine learning models predict and visualise heat distribution to guide mitigation strategies.
  • High-resolution data: Over 250 local sensors deliver real-time microclimatic data, enabling precise mapping of temperature patterns.
  • Integration of multiple sources: Combining sensor networks, satellite imagery, and urban data layers creates a dynamic picture of heat exposure.
  • Decision support for cities: The KLIPS dashboard helps planners evaluate the effects of greening, shading, and material choices before implementation.
  • Community and policy relevance: The approach informs local adaptation strategies and engages citizens through open data.
  • Scalable model: The Dresden prototype provides a replicable framework for other cities facing increasing urban heat stress.

In Dresden, Germany, the KLIPS project demonstrates how artificial intelligence and real-time environmental data can support climate adaptation in cities. As Dr. Hendrik Herold from the KLIPS project explained, the system combines more than 250 temperature sensors with satellite, cadastral, and weather data to map and simulate urban heat islands on a hyperlocal scale – providing urban planners with precise insights for data-driven, climate-resilient urban development.

As climate change intensifies, cities across Europe are experiencing longer and more frequent heatwaves. Dense urban structures, sealed surfaces, and limited green spaces exacerbate these effects, creating local heat islands that threaten health and well-being. KLIPS addresses this challenge by using AI to identify and simulate temperature variations across the urban fabric.

The system continuously collects and analyses temperature data from a dense network of sensors placed on buildings, streetlights, and public infrastructure. These measurements are complemented by high-resolution satellite imagery and cadastral information, feeding into an AI model that forecasts temperature distribution under different scenarios. This enables planners to test the potential cooling impact of trees, reflective materials, or urban design interventions before they are implemented.

A key feature of KLIPS is its interactive dashboard, which visualises the data in accessible maps and timelines. Users can explore temperature changes at the street or district level, compare day-night variations, and simulate the effects of planned interventions. This not only enhances transparency but also bridges scientific insight and municipal decision-making processes.

Supported by the European MAtchUP programme and Germany’s mFUND initiative, KLIPS illustrates how data-driven tools can make urban adaptation tangible and actionable. By linking AI technology with local expertise and open data principles, the project sets a strong 

This event is part of the ISCN Global Mixer, a series of events organized by the International Smart Cities Network. The presentations cover a wide range of topics related to international smart city approaches and provide exciting insights into urban digitalization worldwide - in just 30 minutes.

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