Implementing State-of-the-Art Deep Learning Approaches for Archaeological Object Detection in Remotely-Sensed Data: The Results of Cross-Domain Collaboration
- DOI
- 10.5334/jcaa.78
- Published
- 12
- Container
- Journal of Computer Applications in Archaeology
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.5334/jcaa.78,
title = {Implementing State-of-the-Art Deep Learning Approaches for Archaeological Object Detection in Remotely-Sensed Data: The Results of Cross-Domain Collaboration},
author = {Martin Olivier and Wouter Verschoof-van der Vaart},
year = {2021},
journal = {Journal of Computer Applications in Archaeology},
doi = {10.5334/jcaa.78},
url = {https://doi.org/10.5334/jcaa.78}
}RIS
TY - JOUR TI - Implementing State-of-the-Art Deep Learning Approaches for Archaeological Object Detection in Remotely-Sensed Data: The Results of Cross-Domain Collaboration AU - Martin Olivier AU - Wouter Verschoof-van der Vaart PY - 2021 JO - Journal of Computer Applications in Archaeology DO - 10.5334/jcaa.78 UR - https://doi.org/10.5334/jcaa.78 ER -
APA
Olivier, M., & Vaart, W. V. D. (2021). Implementing State-of-the-Art Deep Learning Approaches for Archaeological Object Detection in Remotely-Sensed Data: The Results of Cross-Domain Collaboration. Journal of Computer Applications in Archaeology. https://doi.org/10.5334/jcaa.78
Source records
- doaj · retrieved 2026-09-26T04:53:21.548Z