January 22, 2019
Archaeology, Machine Learning and the power of crowdsourcing
Research is underway on the combined use of crowdsourcing and machine learning with EO data to systematically detect buried archaeological structures. Crowdsourcing is initially used to create labelled data, taking advantage of human interpretation to identify, often very faint, archaeological crop patterns in remotely sensed imagery. This labelled data will then be used to train a convolutional neural network to systematically identify similar buried structures over a larger area.
As the population of the Earth increases, so does the demand for resources. Development puts at risk the irreplaceable archaeological record. Especially in countries with a rich archaeological heritage, such as in the Mediterranean region, methodologies are sorely needed to increase the efficiency and reduce the costs of archaeological survey.
Participate in the crowdsourcing project to help identify buried archaeological features!
Post contributed by Chris Stewart