Chizhova, MariaMariaChizhova0000-0002-3301-851XPan, JunquanJunquanPanLuhmann, ThomasThomasLuhmannKarami, A.A.KaramiMenna, FabioFabioMennaRemondino, FabioFabioRemondinoHess, MonaMonaHess0000-0001-7298-7631Eißing, ThomasThomasEißing2024-02-262024-02-2620242194-9034https://fis.uni-bamberg.de/handle/uniba/93607Stability of historic wooden constructions is changing with time and should be inspected appropriately for risk assessment and prevention. The stability or strength values of built-in historic timber are difficult or even impossible to be derived without invasive investigation, but this is particularly problematic for the monitoring of heritage objects. Luckily there are some visible timber surface features, like knots and cracks, which can act as individual evidence to estimate the wood strength as well as to adjust its grade class indicator. In the final project, we aim to compare different approaches for 3D digital documentation of historic wood timbers and focus on automatic knot detection using AI techniques. A first feasibility study reported here provides a scientific baseline for the development of an automated method to analyse historic timber stability using 3D surveying and recognised surface features. First results about texture and resolution properties are discussed here.enghistoric wooden timberfeature extractionmachine learning3D scanningstructure from motionmulti view photometric stereoreflectance transformation imaging920conferenceobject10.5194/isprs-archives-xlviii-2-w4-2024-103-2024https://isprs-archives.copernicus.org/articles/XLVIII-2-W4-2024/103/2024/isprs-archives-XLVIII-2-W4-2024-103-2024.pdf