Remote sensing data covering large geographical areas can be easily accessed and are being acquired with greater frequency. The massive volume of data requires an automated image analysis system. By taking advantage of the increasing availability of data using computer vision, we can design specific systems to automate data analysis and detection of archaeological objects. In the past decade, there has been a rise in the use of automated methods to assist in the identification of archaeological sites in remote sensing imagery. These applications offer an important contribution to non-intrusive archaeological exploration, helping to reduce the traditional human workload and time by signalling areas with a higher probability of presenting archaeological sites for exploration. This survey describes the state of the art of existing automated image analysis methods in archaeology and highlights the improvements thus achieved in the detection of archaeological monuments and areas of interest in landscape-scale satellite and aerial imagery. It also presents a discussion of the benefits and limitations of automatic detection of archaeological structures, proposing new approaches and possibilities.

This work was partially supported by the Fundação para a Ciência e a Tecnologia, I.P. (FCT) through the ISTAR-Iscte project UIDB/04466/2020 and UIDP/04466/2020.

Archaeological monuments,Automated detection,Computer vision,Image analysis,Remote sensing

• Earth and related Environmental Sciences - Natural Sciences
• History and Archeology - Humanities

This publication is an output of the following project(s):

• Alternative Shaper
• Impact of Blockchains and Decentralised Ledger Technologies