Deep learning for archaeological object detection in airborne laser scanning data

Abstract

It is important to preserve archaeological monuments as they play a key role in helping us understand human history and their accomplishments for times with no or little written sources. The first step for this purpose is an efficient method for collecting and documenting information about objects of interest for archaeologists. Airborne laser scanning (ALS) is of great use in collecting and documenting detailed measurements from an area of interest. However, it is time consuming for scientists to manually analyze the collected ALS data. One possible way to automate this process is using deep neural networks. In this work, we propose a hierarchical Convolutional Neural Network (CNN) model to..