The use of Deep Learning in the automated detection of archaeological objects in remotely sensed data
Generally the data from remote sensing surveys - the scanning of the earth by satellite or aircraft in order to obtain information about it - is screened manually in archaeology. However, constant monitoring of the earth's surface causes a huge influx of data of high complexity and high quality. To cope with this ever growing set of largely digital and easily available data, computer-aided methods for the processing of data and the detection of archaeological objects are needed.
- Wouter Verschoof
Over a decade ago, archaeologists started developing computational methods for the (semi-) automated detection of archaeological objects. However, these (often) handcrafted algorithms are highly specialized on specific object categories and data sources, which restricts their use in different contexts and limits their usability in general for archaeological prospection. Furthermore, these approaches are predominantly complex algorithms that can require a high level of expertise, and are regularly dependent on expensive software. All this results in a limited user-friendly implementation.
This research project explores the implementation of advanced computational methods to develop a generic, flexible and robust automated detection method for archaeological objects in remotely sensed data. More specifically, this project aims to develop user-friendly workflows for the detection of multiple classes of archaeological objects in LiDAR (Light Detection And Ranging) data using Deep Learning.