A 3D POINT CLOUD DEEP LEARNING APPROACH USING LIDAR TO IDENTIFY ANCIENT MAYA ARCHAEOLOGICAL SITES

Authors

Heather Richards-Rissetto, University of Nebraska-LincolnFollow
David Newton, University of Nebraska - LincolnFollow
Aziza Al Zadjali, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

2021

Citation

Richards, H., D. Newton, A. Al Zadjali. 2021. A 3D POINT CLOUD DEEP LEARNING APPROACH USING LIDAR TO IDENTIFY ANCIENT MAYA ARCHAEOLOGICAL SITES. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VIII-M-1-2021 28th CIPA Symposium “Great Learning & Digital Emotion”, 28 August–1 September 2021, Beijing, China

Comments

© Author(s) 2021. CC BY 4.0 License.

Abstract

Airborne light detection and ranging (LIDAR) systems allow archaeologists to capture 3D data of anthropogenic landscapes with a level of precision that permits the identification of archaeological sites in difficult to reach and inaccessible regions. These benefits have come with a deluge of LIDAR data that requires significant and costly manual labor to interpret and analyze. In order to address this challenge, researchers have explored the use of state-of-the-art automated object recognition algorithms from the field of deep learning with success. This previous research, however, has been limited to the exploration of deep learning processes that work with only 2D data, which excludes the use of available 3D data. Our research addresses this gap and contributes knowledge on the use of deep learning-based processes that can classify archaeological sites from LIDAR generated 3D point cloud datasets. LIDAR data from the UNESCO World Heritage Site of Copan, Honduras is used as the primary dataset to compare the classification accuracy of deep learning models using 2D and 3D data. The results demonstrate that models using 3D point cloud datasets provide the greatest classification accuracy in identifying Maya archaeological sites while requiring less data preparation. Further, the research contributes knowledge on the efficacy of data augmentation strategies when working with small 3D datasets.