GI_Forum 2021, Volume 9, Issue 2
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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 2021, Volume 9, Issue 2 ISSN 2308-1708 Online Edition ISBN 978-3-7001-9183-4 Online Edition
Mohammad Mustafa Sa'doun,
Christopher D. Lippitt,
Gernot Paulus,
Karl–Heinrich Anders
S. 152 - 166 doi:10.1553/giscience2021_02_s152 Verlag der Österreichischen Akademie der Wissenschaften doi:10.1553/giscience2021_02_s152
Abstract: Waterfowl monitoring is an important task for understanding waterfowl distribution and habitats. Surveying approaches using hyper-spatial airborne imagery, collected by small unoccupied aerial systems (sUAS), hold potential to overcome the limitations of traditional methods while improving count efficiency and reliability. Difficulties obtaining waterfowl counts, particularly in complex image scenes, from the high quantity of imagery required hinders deployment of large-scale surveys. In this paper, we test Convolutional Neural Networks (CNNs) to understand their potential and how they behave across different versions of our waterfowl dataset. Three CNN architectures (YOLO, Retinanet and Faster R-CNN) were trained on 3 hierarchical levels: waterfowl detection (True / False), waterfowl type (3 classes), and waterfowl species (8 classes). The architectures generally performed well, and results indicate that automated waterfowl detection in complex environments, and therefore enumeration, is feasible using current technology. Waterfowl identification in complex environments was not successful using the available training data, but we propose steps that might enhance the results. Keywords: waterfowl surveying, YOLO, Retinanet, Faster R-CNN, sUAS, deep learning Published Online: 2021/12/28 12:34:20 Object Identifier: 0xc1aa5576 0x003d25f2 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 |