‘Mark the Graves’: Adapting automation for improving the delineation of graves in GPR data
Channel: Recording Archaeology
Date: 2019-10-25

While GPR has proven successful in detecting recent and archaeological graves, a large proportion of graves still go undiscovered until they are disturbed during excavation owing to coarse survey parameters or even the ability of GPR to detect a range of features in any given environment. The ethical treatment of human remains is culturally important; hence the need to improve the potential of recovering remains prior to their destruction by machine excavation, other anthropogenic activities, or natural processes. Geophysical survey and reporting outputs could be improved by implementing a standardised interactive workflow for determining appropriate survey parameters together with automated classification software. Interactive adaptive guidelines for GPR survey based on environmental and anthropogenic factors and target properties detailed in existing archaeogeophysical survey guidelines will determine appropriate survey parameters to allow for the maximum potential detection of a designated target. The prototype software utilises supervised machine learning to retrain convolutional neural networks from TensorFlow, Google™’s open-source software library. A graphical user interface (GUI) will be available for accessibility if the user requires. Training datasets were derived from rasterised 2D and 3D ground-truthed, real and simulated improved and raw GPR data. Recent progress and future outputs of this ongoing doctoral research project will be presented. Particular attention will be paid to the composition of the training data, nature of the accuracy of the model when compared to the original neural networks.

 (Ashely Green, Paul Cheetham, Timothy Darvill)