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Detecting Historical Terrain Anomalies With UAV-LiDAR Data Using Spline-Approximation and Support Vector Machines

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https://doi.org/10.48693/477
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dc.creatorStorch, Marcel-
dc.creatorde Lange, Norbert-
dc.creatorJarmer, Thomas-
dc.creatorWaske, Björn-
dc.date.accessioned2024-02-05T16:35:33Z-
dc.date.available2024-02-05T16:35:33Z-
dc.date.issued2023-03-20-
dc.identifier.citationM. 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. 3158-3173ger
dc.identifier.urihttps://doi.org/10.48693/477-
dc.identifier.urihttps://osnadocs.ub.uni-osnabrueck.de/handle/ds-2024020510523-
dc.description.abstractThe 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.eng
dc.relationhttps://doi.org/10.1109/JSTARS.2023.3259200ger
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectHistorical terrain anomalieseng
dc.subjectmachine learningeng
dc.subjectsplineseng
dc.subjectUAV-LiDAReng
dc.subjectVegetation mappingeng
dc.subjectRemote sensingeng
dc.subjectCultural differenceseng
dc.subjectSupport vector machineseng
dc.subjectLaser radareng
dc.subjectFiltering algorithmsger
dc.subject.ddc004 - Informatikger
dc.subject.ddc550 - Geowissenschaftenger
dc.titleDetecting Historical Terrain Anomalies With UAV-LiDAR Data Using Spline-Approximation and Support Vector Machineseng
dc.typeEinzelbeitrag in einer wissenschaftlichen Zeitschrift [Article]ger
orcid.creatorhttps://orcid.org/0000-0001-5726-6297-
orcid.creatorhttps://orcid.org/0000-0002-4652-1640-
orcid.creatorhttps://orcid.org/0000-0002-2586-3748-
dc.identifier.doi10.1109/JSTARS.2023.3259200-
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