The documentation and protection of archaeological and cultural heritage (ACH) using
remote sensing, a non-destructive tool, is increasingly popular for experts around the world …

Airborne LiDAR is a widely accepted tool for archaeological prospection. Over the last
decade an archaeology-specific data processing workflow has been evolving, ranging from …

Machine learning (ML) is rapidly being adopted by archaeologists interested in analyzing a
range of geospatial, material cultural, textual, natural, and artistic data. The algorithms are …

Abstract The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved
since basic statistical approaches such as Linear Regression to complex Deep Learning …

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 …

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 …

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) …

In this paper, we report the results of our work on automated detection of qanat shafts on the
Cold War-era CORONA Satellite Imagery. The increasing quantity of air and space-borne …

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 …

The objective of this project is to create a new implementation of a deep learning model that
uses digital elevation data to detect shipwrecks automatically and rapidly over a large …