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A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification
| Content Provider | MDPI |
|---|---|
| Author | Teruggi, Simone Grilli, Eleonora Russo, Michele Fassi, Francesco Remondino, Fabio |
| Copyright Year | 2020 |
| Description | 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. |
| Starting Page | 2598 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs12162598 |
| Journal | Remote Sensing |
| Issue Number | 16 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-08-12 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Machine Learning 3d Architectural Heritage Multi-resolution Point Cloud Classification Random Forest |
| Content Type | Text |
| Resource Type | Article |
