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 …

Although the history of automated archaeological object detection in remotely sensed data is
short, progress and emerging trends are evident. Among them, the shift from rule-based …

Machine Learning-based workflows are being progressively used for the automatic
detection of archaeological objects (intended as below-surface sites) in remote sensing …

Until recently, archeological prospection using LiDAR data was based mainly on
expertbased and time-consuming visual analyses. Currently, deep learning convolutional …

The existing cultural heritage mapping in Norway is incomplete. Some selected areas are
mapped well, while the majority of areas only contain chance discoveries, often with bad …

This paper presents WODAN2. 0, a workflow using Deep Learning for the automated
detection of multiple archaeological object classes in LiDAR data from the Netherlands …

Due to complicated backgrounds and unclear target orientation, automated object detection
is difficult in the field of archaeology. Most of the current convolutional neural network (CNN) …

This article presents a novel deep learning method for semi-automated detection of historic
mining pits using aerial LiDAR data. The recent emergence of national scale remotely …

The systematic mapping of hollow roads, traces of (post) medieval sunken cart tracks ways,
can provide information on past human movement and historical route networks. However …

Airborne LiDAR produced large amounts of data for archaeological research over the past
decade. Labeling this type of archaeological data is a tedious process. We used a data set …