Felipe Lumbreras
Biographic Data
| ID | 6452034 |
|---|---|
| NAME | Felipe Lumbreras |
| GIVEN NAMES | Felipe |
| FAMILY NAME | Lumbreras |
| SIGNATURE | LUMBRERAS F |
| AFFILIATIONS | Universitat Autonòma de Barcelona |
| ORCID | 0000-0003-2887-8053 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 2 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Best practices for the application of computer vision-based machine learning in archaeology
Defines seven best practices for CV-based ML in archaeology. • Links sources, preprocessing, training, post-treatment and validation. • Shows how to reduce false positives in low-density survey settings. • Integrates explainability, cross-validation and ethical responsibility. • Promotes reproducible, FAIR and archaeologically informed AI workflows
Theory and practice of artificial intelligence in archaeology
During the last decade, Machine Learning (ML) approaches have been increasingly adapted to archaeology from related fields, supported by the expansion of open repositories, advances in computational infrastructures, and the fast development of detection, classification and segmentation algorithms. However, the multiplication of ML-based papers can give a false impression of methodological efficiency and general applicability, while masking fundam…
Leveraging Big Multitemporal Multisource Satellite Data and Artificial Intelligence for the Detection of Complex and Invisible Features
The detection of buried or obscured archaeological features remains a central challenge in landscape archaeology, particularly in the irrigated floodplains of Mesopotamia where levees and canals formed the basis of complex agrarian systems. This study presents a deep learning–based approach for the large‐scale, automated detection of ancient levees in central Iraq, integrating big multitemporal and multisource satellite datasets with advanced ins…
Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms
Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms
Best practices for the application of computer vision-based machine learning in archaeology
Defines seven best practices for CV-based ML in archaeology. • Links sources, preprocessing, training, post-treatment and validation. • Shows how to reduce false positives in low-density survey settings. • Integrates explainability, cross-validation and ethical responsibility. • Promotes reproducible, FAIR and archaeologically informed AI workflows
Theory and practice of artificial intelligence in archaeology
During the last decade, Machine Learning (ML) approaches have been increasingly adapted to archaeology from related fields, supported by the expansion of open repositories, advances in computational infrastructures, and the fast development of detection, classification and segmentation algorithms. However, the multiplication of ML-based papers can give a false impression of methodological efficiency and general applicability, while masking fundam…
Leveraging Big Multitemporal Multisource Satellite Data and Artificial Intelligence for the Detection of Complex and Invisible Features
The detection of buried or obscured archaeological features remains a central challenge in landscape archaeology, particularly in the irrigated floodplains of Mesopotamia where levees and canals formed the basis of complex agrarian systems. This study presents a deep learning–based approach for the large‐scale, automated detection of ancient levees in central Iraq, integrating big multitemporal and multisource satellite datasets with advanced ins…
Image Processing and 3D Reconstruction (3 works) · Archaeological Research and Protection (2 works) · Archaeology and ancient environmental studies (2 works) · Algorithm (1 work) · Applications of artificial intelligence (1 work) · Archaeological theory (1 work) · Artificial Intelligence (1 work) · Best evidence (1 work) · Best practice (1 work) · Computer Science (1 work)