Loggia dei Lanzi: AI Thermography Enhancement Comparisons through 3D Photogrammetry
2026-08-03 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionDigital Libraries
AI summaryⓘ
The authors studied the Loggia dei Lanzi in Florence using a thermal camera to find hidden parts of the building that are not visible from the outside. They tested different ways to make the thermal images clearer, including special camera technology and AI-based enhancement. They checked if these clearer images helped create better 3D models of the structure. Their work helps show how AI and new imaging methods can improve the study of historic buildings. They also shared all their data in a public 3D format and included it in a citywide augmented reality app.
thermal imagingLoggia dei Lanzisuper-resolutionartificial intelligencephotogrammetryStructure-from-Motionheritage documentation3D modelingaugmented realitythermal camera
Authors
Scott McAvoy, Jonathan Klingspon, George Bent, Dave Pfaff, Aviral Agarwal, Maurizio Seracini, Falko Kuester
Abstract
The Loggia dei Lanzi in the Piazza della Signoria is one of Florence's most prominent structures visited by millions every year. Its construction history spans multiple centuries of modification. This paper presents the results of a thermal imaging campaign conducted in December 2025, using a FLIR T1020 HD camera, revealing hidden architectural features including walled-up openings and material transitions beneath the plaster surface. The favorable winter ambient conditions provided a feature-rich benchmark upon which to compare the results of enhancement algorithms and artificial intelligence models. We evaluate the application of AI-based image enhancement to thermal heritage documentation through a comparison of three tiers of image resolution in a photogrammetric Structure-from-Motion (SfM) pipeline: native resolution, FLIR's hardware-based pixel-shifted super-resolution (UltraMax), and state of the art AI-upscaled imagery models. We quantify the effect of each resolution tier on feature detection and tie-point generation, assessing whether the additional detail produced by super-resolution, whether hardware or AI-derived, translates into meaningfully denser and more accurate 3D thermal models. Our results contribute to the emerging intersection of artificial intelligence and heritage thermography by providing a direct comparison of hardware microscanning and AI super-resolution within a thermal photogrammetric workflow for cultural heritage. All datasets are made publicly available and accessible within an interactive 3D archival framework, and integrated into a custom citywide extended reality overlay application.