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dc.contributor.authorAkbulut, Zeynep
dc.contributor.authorKarsli, Fevzi
dc.contributor.authorDihkan, Mustafa
dc.date.accessioned2023-12-28T11:34:21Z
dc.date.available2023-12-28T11:34:21Z
dc.date.issued2023en_US
dc.identifier.citationAkbulut, Z., Karsli, F. & Dihkan, M. A New Approach for Ground Filtering of Airborne Laser Scanning Data Using PointNet++. J Indian Soc Remote Sens (2023). https://doi.org/10.1007/s12524-023-01794-xen_US
dc.identifier.urihttps://link.springer.com/article/10.1007/s12524-023-01794-x
dc.identifier.urihttps://hdl.handle.net/20.500.12440/6125
dc.description.abstractGround filtering is an essential process that separates ground and non-ground points for generating digital elevation models (DEMs) from airborne laser scanning (ALS) point cloud data. Traditionally, ground filtering was carried out using rule-based approaches, but with the advancements in deep learning networks, ground filtering can now be performed using these methods. Deep neural networks (DNNs) require large amounts of training samples to reveal key information within the dataset. OpenGF is a new ultra-large-scale dataset which has nearly half a billion points from nine different scenes ranging from highly dense urban areas to steep vegetation covered mountains for ground filtering task. In this study, PointNet++ architecture was employed for ground filtering on the OpenGF dataset. Firstly, we slightly modified PointNet++ architecture's sampling strategy to decrease the number of duplicate points in the batching process. We proposed a new adaptive tiling strategy based on input point cloud density to cope with highly varying point sampling of the OpenGF dataset. Modified PointNet++ architecture was trained with both adaptive tiling strategy and original OpenGF tiles to investigate the advantages and drawbacks of the proposed strategy on the ground filtering task. To evaluate the performance of the proposed strategy, the results for different test areas were compared with those obtained using the original PointNet++ and rule-based cloth simulation filtering (CSF) algorithms. Despite using fewer epochs, the proposed data collection strategy achieved up to 13% higher accuracy on test scenes with the modified PointNet++ architecture compared to the network trained with original OpenGF tiles.en_US
dc.language.isoengen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofJOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSINGen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAirborne laser scanningen_US
dc.subjectDeep neural networken_US
dc.subjectGround filteringen_US
dc.subjectPoint clouden_US
dc.subjectPointNet plus plusen_US
dc.subjectSemantic segmentationen_US
dc.titleA New Approach for Ground Filtering of Airborne Laser Scanning Data Using PointNet plusen_US
dc.typearticleen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.description.wospublicationidWOS:001122634900001en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Harita Mühendisliği Bölümüen_US
dc.authorid0000-0001-9801-1506en_US
dc.contributor.institutionauthorAkbulut, Zeynep
dc.identifier.doi10.1007/s12524-023-01794-xen_US
dc.authorwosidJAD-2101-2023en_US
dc.authorscopusid57270443800en_US
dc.description.wosqualityQ3en_US


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