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2024 - 2026

STATUS

VALLETTA, MALTA

LOCATION

AP VALLETTA

NEURAL AI

UNIVERSITY OF MALTA

COLLABORATORS

LIMESTONE INTELLIGENT MAPPING AND 
        AUTOMATED PROCESS

Bringing together architectural expertise and machine learning

to test how AI

can supplement

professional judgment.

An applied research project that uses an AI-driven system for automated identification and classification of limestone deterioration across imaging platforms.

COLLABORATORS

AP Valletta

Neural AI

University of Malta

LIMAP, which stands for ‘Limestone Intelligent Mapping and Automated Process’, is a research project developed under the Research Excellence Programme – a national research and innovation funding programme designed to support early-stage, high-quality research projects for innovative work that generates new knowledge and broader impact. LIMAP explores how artificial intelligence might support the identification and classification of deterioration in limestone masonry, particularly within heritage contexts, where nuanced analysis is critical.

 

The project brings together architectural expertise and machine learning to test how AI can supplement professional judgment. Initial stages of the project have focused on moving toward a practical, scalable workflow in which AI-assisted mapping can support conservation planning, condition assessment, and long-term maintenance strategies. By aligning computational precision with human, architectural understanding and reasoning, the project lays the groundwork for a more proactive and informed approach to limestone heritage management.

Internationally, research into AI-assisted heritage diagnostics is expanding. Studies have applied software learning models to detect and classify stone decay, masonry defects, and surface anomalies from photographs, photogrammetry, and 3D reconstructions. Some projects also integrate segmentation tools with point cloud analysis to automate condition mapping. LIMAP’s ambition expressly lies in combining, not replacing, architectural expertise with machine learning. It is the first system locally in Malta that is working to support systematic, scalable deterioration assessment in the wider ecology of heritage conservation practice.

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