GIS-based solution for sustainable heritage maintenance
GIS-based tool to plan sustainable maintenance while respecting heritage value.
This service provides a geospatial, data-driven approach to maintaining and preserving cultural heritage buildings sustainably. It enables municipalities, researchers, and heritage managers to explore architectural typologies, identify shared characteristics, and design renovation strategies that improve energy performance and comfort, without compromising historical authenticity.
Key Features
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AI-driven clustering
Group cultural heritage buildings based on shared architectural and structural traits for pattern analysis.
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Interactive GIS map
Visualise building clusters, attributes, and trends across cities in a dynamic interface.
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Search and filtering tools
Explore features such as architectural style, materials, or renovation status with ease.
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Feature-level metadata
Access detailed information for each listed building, including use, year, materials, and condition.
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Integrated storytelling interface
Combine maps, narrative text, and multimedia to provide an intuitive exploration experience.
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Multi-pilot deployment
The storymap currently covers multiple cities, including Athens, with Visby and Riga to be added soon. It has scalable potential for broader applications.
Benefits
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Prioritises maintenance or retrofitting strategies based on data
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Enables cities to plan coordinated, resource-efficient renovation programs
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Supports energy-efficient maintenance while preserving the authenticity of historic buildings
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Enables benchmarking and sharing of best practices
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Designed both for professionals and the general public
How it works
Information is gathered from national heritage databases and municipal records, covering construction year, architectural style, original and current use, materials, structural elements, number of floors, renovation status, and decorative features. In addition, AI-based image analysis is used to extract visual decorative and architectural features directly from building photographs, enriching the dataset with visual descriptors. Using unsupervised machine learning, buildings are clustered based on shared typological and structural characteristics, revealing patterns that can inform sustainable maintenance and energy retrofitting strategies.
The processed data is presented in a web-based GIS interface built with Esri’s ArcGIS suite, integrating ArcGIS Pro for geospatial analysis and 2D/3D visualisation, ArcGIS Online for cloud-based hosting, ArcGIS Experience Builder for dynamic web applications, and ArcGIS StoryMaps for interactive storytelling.
Users can explore pilot cities through interactive maps, click on buildings to access detailed metadata, apply filters by architecture or function, follow pilot-specific storylines, and navigate full-screen views. The system generates interactive visual analytics and building-level reports, providing insights into typological trends and risk factors that guide sustainable maintenance planning, prioritise interventions, and optimise conservation resources.
