Computer Vision for archaeology
Archaeological Tumuli detection
A hybrid algorithm that combines Deep Learning and Machine Learning to improve the automatic detection of archaeological tumuli avoiding the inclusion of most false positives.
The fist results have been published at the Remote Sensing journal. In this article, researchers give more information on the data analyzed and the performance of this innovative computer-based automatic detection initiative.
– The area covered is the largest (to the extent of our knowledge) in which archaeological DL approaches have ever been applied and it covers almost 30,000 km2
-10,527 objects have been detected of which approximately 9,422 correspond to archaeological tumuli (after careful visual validation with high-resolution imagery and pending ground validation). That is, 89.5% of the detected tumuli correspond to true positives.
-We have only employed open source data in this research. However, the use of higher resolution data, in particular higher resolution satellite imagery instead of the Sentinel 2 (10m/px) images employed, would radically decrease the number of false positives reaching a success rate above 97%.
-Code, sources and results (including validation) are freely available and the code is designed to be used in freely accessible cloud computing platforms Google Colaboratory and Earth Engine) so the lack of computational resources will not pose a problem for its application to other study areas (even very large ones).
This approach provides a way forward for the detection of tumuli avoiding the inclusion of most false positives. The algorithm can be applied in areas of the world where topographic data of enough resolution are available. Providing specific training data, this hybrid approach can also be used to detect other types of features where a large number of false positives are an issue.
Automated detection and classification of multi-cell phytoliths
A new algorithm for the automated detection and classification of multi-cell phytoliths with a high level of accuracy, up to a species level.
The article, in which collaborates the CVC researcher Dr Felipe Lumbreras, together with researchers from the Landscape Archaeology Research Group (GIAP-ICAC), the Catalan Institute of Classical (ICAC), the University of Toronto Mississauga and the McDonald Institute for Archaeological Research of the University of Cambridge, has been published in the Journal of Archaeological Science.
The proposed method has the potential to allow the development of much larger analytical datasets in a fraction of the time than was previously feasible, as well as to assure consistency in phytolith identification and increase the validity of sample analysis.