Valentino Vitale
Biographic Data
| ID | 4354834 |
|---|---|
| NAME | Valentino Vitale |
| GIVEN NAMES | Valentino |
| FAMILY NAME | Vitale |
| SIGNATURE | VITALE V |
| AFFILIATIONS | University of Basilicata |
| ORCID | 0000-0002-0006-8633 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
An Open-Source Machine Learning–Based Methodological Approach for Processing High-Resolution UAS LiDAR Data in Archaeological Contexts
This study shows and discusses an innovative approach devised for archaeological feature detection using unmanned aerial system (UAS) LiDAR and an open-source probabilistic machine learning framework. The methodology employs a Random Forest classification algorithm within CloudCompare’s 3DMASC plugin to analyse dense LiDAR point clouds. The main steps include classifier training, hyperparameter adjustment and point cloud segmentation to produce d…
The case of the middle valley of the Sinni (Southern Basilicata). Methods of archaeological and architectural documentation
An Open-Source Machine Learning–Based Methodological Approach for Processing High-Resolution UAS LiDAR Data in Archaeological Contexts
This study shows and discusses an innovative approach devised for archaeological feature detection using unmanned aerial system (UAS) LiDAR and an open-source probabilistic machine learning framework. The methodology employs a Random Forest classification algorithm within CloudCompare’s 3DMASC plugin to analyse dense LiDAR point clouds. The main steps include classifier training, hyperparameter adjustment and point cloud segmentation to produce d…
The case of the middle valley of the Sinni (Southern Basilicata). Methods of archaeological and architectural documentation
An Open-Source Machine Learning–Based Methodological Approach for Processing High-Resolution UAS LiDAR Data in Archaeological Contexts
This study shows and discusses an innovative approach devised for archaeological feature detection using unmanned aerial system (UAS) LiDAR and an open-source probabilistic machine learning framework. The methodology employs a Random Forest classification algorithm within CloudCompare’s 3DMASC plugin to analyse dense LiDAR point clouds. The main steps include classifier training, hyperparameter adjustment and point cloud segmentation to produce d…
3D Surveying and Cultural Heritage (2 works) · Archaeological Research and Protection (2 works) · Archaeology (2 works) · Computer Science (2 works) · Geography (2 works) · World Wide Web (2 works) · Artificial Intelligence (1 works) · Conservation Techniques and Studies (1 works) · Documentation (1 works) · Engineering (1 works)