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Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning | IEEE Conference Publication | IEEE Xplore

Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning


Abstract:

The advent of LiDAR technology has had a revolutionary impact on archaeological prospection by vastly enlarging the coverage of ancient landscapes and consequently the nu...Show More

Abstract:

The advent of LiDAR technology has had a revolutionary impact on archaeological prospection by vastly enlarging the coverage of ancient landscapes and consequently the number of ancient surface features. However, manual analysis by experts requires a significant time and money investment. This paper describes a deep learning model developed to segment, i.e., label, the semantics of objects of interest as a means to augment or supplant manual labeling of LiDAR data. The U-Net deep learning model forms the backbone of the system which has shown success in providing accurate outputs on similar LiDAR data set. The trained U-Net model is integrated into an inference pipeline to transform expansive LiDAR datasets into labeled output images. Work focuses on the classification of two semantic types: (1) platforms and (2) annular structures whose attributes, e.g., location, shape, and distribution, play an important role in improving our understanding of ancient Maya civilizations. This article provides a deep learning-based system that efficiently extracted these structures. CNN-generated inferences were compared against expert-labeled features to measure algorithm performance. Results for a LiDAR survey of 479 sq. km. indicate that the CNN provides an IoU performance of 0.82 and 0.74 for annular structures and platforms respectively. The discussion further analyzes how IoU performance relates to the viability of this approach as an aid or substitute for manual labeling.
Published in: SoutheastCon 2023
Date of Conference: 01-16 April 2023
Date Added to IEEE Xplore: 08 May 2023
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Conference Location: Orlando, FL, USA

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