Machine Learning for Cultural Heritage Classification
Michele Russo, Eleonora Grilli, Fabio Remondino, Simone Teruggi, Francesco Fassi · FrancoAngeli srl eBooks · 2021
Cultural Heritage (CH) assets may be defined as integrated spatial systems composed of interconnected shapes.The classification and organization of geometries within a hierarchical system are functional to their correct interpretation, which is often performed using 3D point clouds.The recurring shapes recognition becomes a crucial activity, nowadays accelerated by Machine Learning (ML) procedures able to associate semantic meaning to geometric data.An interdisciplinary research team [1] has developed a ML supervised approach, tested on the Milan Cathedral and Pomposa Abbey datasets, which presents an innovative multi-level and multi-resolution classification (MLMR) process.The methodology improves the learning activity and optimizes the 3D classification by a hierarchical concept.