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 …

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 ever-increasing amount of remotely-sensed data pertaining to archaeology renders
human-based analysis unfeasible, especially considering the expert knowledge required to …

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 …

Mapping potential archaeological sites using remote sensing and artificial intelligence can
be an efficient tool to assist archaeologists during project planning and fieldwork. This paper …

Abstract Within archaeological prospection, Deep Learning algorithms are developed to
detect objects within large remotely sensed datasets. These approaches are generally …

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 …

Today, the advances in airborne LIDAR technology provide high‐resolution datasets that
allow specialists to detect archaeological features hidden under wooded areas more …

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 …