Journal
REMOTE SENSING
Volume 12, Issue 16, Pages -Publisher
MDPI
DOI: 10.3390/rs12162598
Keywords
machine learning; 3D architectural heritage; multi-resolution; point cloud; classification; Random Forest
Categories
Funding
- project Artificial Intelligence for Cultural Heritage (AI4CH) joint Italy-Israel lab - Italian Ministry of ForeignAffairs and International Cooperation (MAECI)
Ask authors/readers for more resources
The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available