Deep Learning for Archaeological Object Detection in Airborne Laser Scanning Data

authored by: Bashir Kazimi, Frank Thiemann, Katharina Malek, Monika Sester, Kourosh Khoshelham
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 classify archaeological objects in ALS data. The data is acquired from the Harz mining Region in Lower Saxony, where a high density of different archaeological monuments including the UNESCO world heritage site Historic Town of Goslar, Mines of Rammelsberg, and the Upper Harz Water Management System can be found. To compare and validate our method, we run experiments on the same data set using two existing deep learning models. The first model is VGG-16; an image classification network pretrained on ImageNet2 data. The second model is a stacked autoencoders model. The results of the classification as analyzed in this paper show that our model is suitably tuned for this task as it achieves the best classification accuracy of around 91 percent, compared to 88 percent and 82 percent accuracy by the pretrained and stacked autoencoders models, respectively.
Organisation(s): Institute of Cartography and Geoinformatics
External Organisation(s): University of Melbourne
Lower Saxon State Agency of Monument Preservation
Type: Conference contribution
Pages: 21-35
No. of pages: 15
Publication date: 2018
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Computer Science(all)
Sustainable Development Goals: SDG 11 - Sustainable Cities and Communities
Electronic version(s): https://ceur-ws.org/Vol-2230/paper_03.pdf (Access: Open)