Digital Representation and AI-driven Virtual Experience for Historic Houses. the Case Study of Borgo Storico Seghetti Panichi

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Abstract

The paper presents the digital representation and virtual experience of Borgo Storico Seghetti Panichi, a cultural site distinguished by the close interplay between architecture and landscape. The project was developed using an integrated approach that brings together 3D survey techniques, immersive technologies, and Artificial Intelligence (AI). Consolidated techniques such as Terrestrial Laser Scanning (TLS) and UAV-based photogrammetry were adopted to generate accurate 3D models of the Villa, the Roccolo, the Church, and the surrounding historic park. Moreover, preliminary tests with 3D Gaussian Splatting (GS) were conducted to evaluate its potential for photorealistic and real-time visualization in Cultural Heritage (CH) contexts. The different digital representations were embedded in a Virtual Reality (VR) application designed to offer an engaging and educational user experience. The immersive environment includes interactive components such as the digital replicas of the buildings and the historic park, audio narration, and a virtual keyboard connected to a Large Language Model (LLM). This system enables users to formulate questions in natural language and receive dynamic, context-specific responses within the VR environment, fostering active learning and customized exploration. The development process included a structured pipeline from survey data to VR implementation, with specific focus on optimization for standalone headsets. User evaluation tests confirmed the effectiveness of the experience in terms of realism, interaction, and accessibility, while also identifying areas for further improvement.

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Clini, P., Angeloni, R., D’Alessio, M., Coppetta, L., & Galli, I. (2025). Digital Representation and AI-driven Virtual Experience for Historic Houses. the Case Study of Borgo Storico Seghetti Panichi. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 48, pp. 337–344). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLVIII-M-9-2025-337-2025

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