Knowledge is Power
In the context of existing buildings—many of which are now decades old and due for refurbishment—access to reliable, structured knowledge becomes essential. These structures contain valuable materials, components, and embedded carbon, yet too often, the data needed to evaluate their reuse potential is fragmented, outdated, or missing entirely.
The first step in extending the life of a building is to create a clear and consistent taxonomy. By systematically identifying and mapping elements such as materials, fixtures, and systems, we can move beyond guesswork and toward decisions based on real-world data. This is especially vital for circular construction practices like reuse and toxicity mapping.
At map.D, our goal is to make this process not only accurate but accessible. Using AI-powered image recognition and speech-to-text, map.D automates the collection and classification of building data, making it easy for surveyors, architects, and even non-specialists to contribute to high-quality building documentation. The result is a centralized, living model of the building—one that supports refurbishment planning, compliance, and ongoing facility management.
Unlike traditional methods that culminate in static PDF reports, map.D enables continuous updates and integration with FDV systems, turning building documentation into a dynamic resource. This shift not only reduces waste and planning errors but also unlocks the value of existing materials—supporting more sustainable, circular practices across the built environment.
By equipping users with simple yet powerful tools, map.D is helping to build a shared foundation of knowledge—one that extends the life of our buildings, improves collaboration across disciplines, and supports smarter decisions at every stage of the building lifecycle.
Our Story
Map.D is a spin-off from Again X, developed as part of the Horizon Europe research project JPI Conect, co-financed by the Norwegian Research Council.
To build resilient cities and create a flourishing, sustainable urban society, we must develop better and more interconnected information networks about the built environment.
The exponential progress of Artificial Intelligence—particularly through large language models (LLMs) and machine learning (ML) - has enabled the integrated transfer of knowledge. We call this Aggregated Information.
This foundation led to the development of the core technologies behind Map.D—a personal assistant with powerful capabilities. It can understand materials visually, sort building components according to Norwegian building codes (NS 3451), convert conversations into structured report formats, and organise data into shareable outputs—either as reports or integrated directly into FDV.
More importantly, Map.D helps establish the infrastructure needed for continuous updates to building data. It transforms FDV into an active exchange platform—no longer just a storage tool, but a living system that evolves alongside your building.
Meet The Team
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