The use of machine learning techniques for point cloud classification has been investigated extensively in the last decade in the geospatial community, while in the cultural heritage field it has only recently started to be explored. The high complexity and heterogeneity of 3D heritage data, the diversity of the possible scenarios, and the different classification purposes that each case study might present, makes it difficult to realise a large training dataset for learning purposes. An important practical issue that has not been explored yet, is the application of a single machine learning model across large and different architectural datasets. This paper tackles this issue presenting a methodology able to successfully generalise to unseen scenarios a random forest model trained on a specific dataset. This is achieved looking for the best features suitable to identify the classes of interest (e.g., wall, windows, roof and columns).
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Grilli, E.; Remondino, F. Machine Learning Generalisation across Different 3D Architectural Heritage. ISPRS Int. J. Geo-Inf.2020, 9, 379.
Grilli E, Remondino F. Machine Learning Generalisation across Different 3D Architectural Heritage. ISPRS International Journal of Geo-Information. 2020; 9(6):379.
Grilli, Eleonora; Remondino, Fabio. 2020. "Machine Learning Generalisation across Different 3D Architectural Heritage." ISPRS Int. J. Geo-Inf. 9, no. 6: 379.
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