A Machine Learning approach to aerial photointerpretation and mapping |
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| I. Cacciari, G.F. Pocobelli, S. Siano |
- Abstract:
- In the project ARCHEO 3.0 a Machine Learning (ML) system for automatic contouring of the stratigraphic units of an archaeological excavation has been experimented. In this research, we have applied the same ML algorithm to aerial color photographs that represent very important tools in the study of ancient topography and landscape archaeology. Aerials of the Vulci necropolis, one of the most important cities of ancient Etruria, have been used. These photos, both vertical and oblique, have been chosen because the marks had been studied and analyzed in a recent PhD work in Ancient Topography. In particular, the traditional mapping method has been compared with the results obtained by means of automated ML algorithm. This experiment has demonstrated that the developed ML algorithm can be applied to aerial photographs for the recognition of archaeological traces, with interesting development prospects. Keyword: Machine Learning, aerial photography, archaeological mapping, landscape archaeology, ancient topography, crop-marks, Vulci.
- Download:
- IMEKO-TC4-METROARCHAEO-2019-81.pdf
- DOI:
- -
- Event details
- IMEKO TC:
- TC4
- Event name:
- TC4 MetroArchaeo 2019
- Title:
IMEKO TC4 International Conference on Metrology for Archaeology and Cultural Heritage
- Place:
- Florence, ITALY
- Time:
- 04 December 2019 - 06 December 2019