Digital Archaeology: Detection of Archaeological Structures Using Convolutional Neural Networks on Aerial LiDAR Data

Authors

Katie LaRue

Senior Project Advisor

Brian Hutchinson

Document Type

Project

Publication Date

Winter 2023

Keywords

Archaeology, Machine Learning, Computer Science, LiDAR, Semantic Segmentation

Abstract

Archaeology is a field that is mostly done by hand. Archaeologists explore remote and unknown areas of the world to find undiscovered civilizations that will give us any idea about how people lived in the past. To speed up this process, Airborne light detection and ranging or LiDAR systems have been used to great effect to speed up this processing. However, we still require domain experts to annotate this information to confirm structures. Deep learning has the potential to speed up this process and the following presentation is a basic overview of machine learning, popular types of deep learning models, as well as an exploration into my own project as part of a research group at Western Washington University.

Department

Computer Science

Recommended Citation

LaRue, Katie, "Digital Archaeology: Detection of Archaeological Structures Using Convolutional Neural Networks on Aerial LiDAR Data" (2023). WWU Honors College Senior Projects. 639.
https://cedar.wwu.edu/wwu_honors/639

Subjects - Topical (LCSH)

Archaeology; Machine learning; Computer science; Optical radar

Type

Text

Rights

Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.

Language

English

Format

application/pdf