ARGUS draws on the current challenges in monitoring remote built heritage assets and the current research focus on preventive preservation, and envisions the development of: (a) a novel built heritage digital twin model to support multi-scale/modal data; (b) an advanced digitisation strategy to support the digital twin model; (c) a portable measurements system for non-descructive physical and chemical monitoring based on miniaturised sensors, and sensor composites integration, comprising of ground and aereal components; (d) AI-enabled methods for the modeling and identification (reverse engineer) of threat factors and their impact; (e) AI-powered multimodal data methods for the fusion of (i) remote sensing climate, weather and pollution data with (ii) natural disaster regional statistics, (iii) governmental statistics (ii) on-site acquired measurements; (f) Trustworthy AI decision support methods for the preventive preservation of built heritage. ARGUS’ innovation targets: (a) Researchers/academics: data from the ARGUS monitoring systems, long-term status processed data, the novel multimodal digital twin white paper, multidimensional/modal data visualisations, APIs. (b) Stakeholders/heritage managers/practitioners: real-time monitoring, long-term status analysis, predictive preser¬vation predictions, intervention decision support. (c) Authorities: real-time monitoring, long-term status analysis, predictive preservation strategies. (d) General public: real-time visualisations, crowdsourcing and citizen contribution in preventive preservation, citizens-in-the-loop R&I design with Living Labs and Hackathons.