Title:	Detecting Historical Terrain Anomalies With UAV-LiDAR Data Using Spline-Approximation and Support Vector Machines
Authors:	Storch, Marcel de Lange, Norbert Jarmer, Thomas Waske, Björn
ORCID of the author:	https://orcid.org/0000-0001-5726-6297 https://orcid.org/0000-0002-4652-1640 https://orcid.org/0000-0002-2586-3748
Abstract:	The documentation of historical remains and cultural heritage is of great importance to preserve historical knowledge. Many studies use low-resolution airplane-based laser scanning and manual interpretation for this purpose. In this study, a concept to automatically detect terrain anomalies in a historical conflict landscape using high-resolution UAV-LiDAR data was developed. We applied different ground filter algorithms and included a spline-based approximation step in order to improve the removal of low vegetation. Due to the absence of comprehensive labeled training data, a one-class support vector machine algorithm was used in an unsupervised manner in order to automatically detect the terrain anomalies. We applied our approach in a study site with different densities of low vegetation. The morphological ground filter was the most suitable when dense near-ground vegetation is present. However, with the use of the spline-based processing step, all filters used could be significantly improved in terms of the F1-score of the classification results. It increased by up to 42% points in the area with dense low vegetation and by up to 14% points in the area with sparse low vegetation. The completeness (recall) reached maximum values of 0.8 and 1.0, respectively, when taking into account the results leading to the highest F1-score for each filter. Therefore, our concept can support on-site field prospection.
Citations:	M. Storch, N. de Lange, T. Jarmer and B. Waske, 2023: Detecting Historical Terrain Anomalies With UAV-LiDAR Data Using Spline-Approximation and Support Vector Machines, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 3158-3173
URL:	https://doi.org/10.48693/477 https://osnadocs.ub.uni-osnabrueck.de/handle/ds-2024020510523
Subject Keywords:	Historical terrain anomalies; machine learning; splines; UAV-LiDAR; Vegetation mapping; Remote sensing; Cultural differences; Support vector machines; Laser radar; Filtering algorithms
Issue Date:	20-Mar-2023
License name:	Attribution 4.0 International
License url:	http://creativecommons.org/licenses/by/4.0/
Type of publication:	Einzelbeitrag in einer wissenschaftlichen Zeitschrift [Article]
Appears in Collections:	FB06 - Hochschulschriften Open-Access-Publikationsfonds

This item is licensed under a Creative Commons License