Learning to Look at LiDAR: The Use of R-CNN in the Automated Detection of Archaeological Objects in LiDAR Data from the Netherlands

Abstract

Computer-aided methods for the automatic detection of archaeological objects are needed to cope with the ever-growing set of largely digital and easily available remotely sensed data. In this paper, a promising new technique for the automated detection of multiple classes of archaeological objects in LiDAR data is presented. This technique is based on R-CNNs (Regions-based Convolutional Neural Networks). Unlike normal CNNs, which classify the entire input image, R-CNNs address the problem of object detection, which requires correctly localising and classifying (multiple) objects within a larger image. We have incorporated this technique into a workflow, which enables the preprocessing of LiDAR data into the required data format and the conversion of the results of the object detection into geographical data, usable in a GIS environment. The proposed technique has been trained and tested on LiDAR data gathered from the central part of the Netherlands. This area contains a multitude of archaeological objects, including prehistoric barrows and Celtic fields. The initial experiments show that we are able to automatically detect and categorise these two types of archaeological objects and thus proof the added value of this technique.