Comparative Analysis of Algorithms to Cleanse Soil Micro-Relief Point Clouds

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autorschaft

Externe Organisationen

  • Karlsruher Institut für Technologie (KIT)
  • Technische Universität Clausthal
  • Johann Heinrich von Thünen-Institut, Bundesforschungsinstitut für Ländliche Räume, Wald und Fischerei
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Details

OriginalspracheEnglisch
Seiten (von - bis)501-521
Seitenumfang21
FachzeitschriftGeomatics
Jahrgang3
Ausgabenummer4
PublikationsstatusVeröffentlicht - 26 Nov. 2023

Abstract

Detecting changes in soil micro-relief in farmland helps to understand degradation processes like sheet erosion. Using the high-resolution technique of terrestrial laser scanning (TLS), we generated point clouds of three 2 × 3 m plots on a weekly basis from May to mid-June in 2022 on cultivated farmland in Germany. Three well-known applications for eliminating vegetation points in the generated point cloud were tested: Cloth Simulation Filter (CSF) as a filtering method, three variants of CANUPO as a machine learning method, and ArcGIS PointCNN as a deep learning method, a sub-category of machine learning using deep neural networks. We assessed the methods with hard criteria such as F1 score, balanced accuracy, height differences, and their standard deviations to the reference surface, resulting in data gaps and robustness, and with soft criteria such as time-saving capacity, accessibility, and user knowledge. All algorithms showed a low performance at the initial measurement epoch, increasing with later epochs. While most of the results demonstrate a better performance of ArcGIS PointCNN, this algorithm revealed an exceptionally low performance in plot 1, which is describable by the generalization gap. Although CANUPO variants created the highest amount of data gaps, we recommend that CANUPO include colour values in combination with CSF.

ASJC Scopus Sachgebiete

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Comparative Analysis of Algorithms to Cleanse Soil Micro-Relief Point Clouds. / Ott, Simone; Burkhard, Benjamin; Harmening, Corinna et al.
in: Geomatics, Jahrgang 3, Nr. 4, 26.11.2023, S. 501-521.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Ott, S, Burkhard, B, Harmening, C, Paffenholz, J-A & Steinhoff-Knopp, B 2023, 'Comparative Analysis of Algorithms to Cleanse Soil Micro-Relief Point Clouds', Geomatics, Jg. 3, Nr. 4, S. 501-521. https://doi.org/10.3390/geomatics3040027
Ott, S., Burkhard, B., Harmening, C., Paffenholz, J.-A., & Steinhoff-Knopp, B. (2023). Comparative Analysis of Algorithms to Cleanse Soil Micro-Relief Point Clouds. Geomatics, 3(4), 501-521. https://doi.org/10.3390/geomatics3040027
Ott S, Burkhard B, Harmening C, Paffenholz JA, Steinhoff-Knopp B. Comparative Analysis of Algorithms to Cleanse Soil Micro-Relief Point Clouds. Geomatics. 2023 Nov 26;3(4):501-521. doi: 10.3390/geomatics3040027
Ott, Simone ; Burkhard, Benjamin ; Harmening, Corinna et al. / Comparative Analysis of Algorithms to Cleanse Soil Micro-Relief Point Clouds. in: Geomatics. 2023 ; Jahrgang 3, Nr. 4. S. 501-521.
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