GI_Forum 2018, Volume 6, Issue 1 Journal for Geographic Information Science
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2018, Volume 6, Issue 1 Journal for Geographic Information Science
ISSN 2308-1708 Online Edition ISBN 978-3-7001-8359-4 Online Edition
Andrew C. Loerch,
Gernot Paulus,
Christopher D. Lippitt
Volumetric Change Detection with using Structure from Motion – The Impact of Repeat Station Imaging ()
S. 135 - 151doi:10.1553/giscience2018_01_s135 Verlag der Österreichischen Akademie der Wissenschaften doi:10.1553/giscience2018_01_s135
Abstract: Repeat Station Imaging (RSI) for image acquisition is compared with non-RSI to assess the methods’ effects on vertical and volumetric estimation using structure from motion (SFM). Aerial triangulation (i.e., SFM) is used to create three-dimensional reconstructions of the study area using unmanned aerial vehicle-acquired imagery. Targets of known volume were deployed throughout the scene and manipulated to create changes between the first and subsequent flights. An RSI flight and two non-RSI flights were compared to a baseline flight in order to estimate a series of introduced volumetric changes, which were then compared to known volume changes. Using images with a nominal ground sampling distance of 1.96 cm, results show a total root-mean-squared-error (RMSE) of 0.035 m3 and mean percent error (MPE) of 25.9% for the RSI flight, and average RMSE of 0.057 m3 and MPE 33.3% for the two non-RSI flights. For the measurement of volumetric changes to extant features, the RSI flight had an RMSE of 0.026 m3 and an MPE of 17.6%; the average RMSE and MPE of the two non-RSI flights were 0.071 m3 and 39.4%. These results show that RSI has the potential to improve the accuracy of volumetric and height change estimation. Keywords: structure from motion, change detection, unmanned aerial vehicle, digital surface model (abbreviations1) Published Online: 2018/07/02 07:33:13 Document Date: 2018/06/22 07:45:00 Object Identifier: 0xc1aa5572 0x00390cd0 Rights:https://creativecommons.org/licenses/by-nd/4.0/
GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |