• Adrijana CAR - Josef STROBL - Robert VOGLER - Gerald GRIESEBNER (Eds.)

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

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.


Starting 2016, GI_Forum publishes two issues a Year.
Joumal Information is available at: GI-Forum

GI_Forum is listed on the Directory of Open Access Journals (DOAJ)

In the 2021-2 issue, the following three thematic areas are addressed: Advances in GIScience, Geo-Social Analytics, and Learning and Education with Geomedia. The contributions, for example, evaluate modes of generalization in animated choropleth maps; investigate different Convolutional Neural Networks (CNNs) architectures for monitoring wildlife in complex natural habitats; introduce an Augmented Reality learning environment for Geoinformatics education; investigate pedestrian infrastructure in a city considering the COVID-19 restrictions; or propose a customised workflow for place extraction from social media text and subsequent geocoding; or address various mobility issues for sustainable cities.

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GI_Forum 2021, Volume 9, Issue 2

ISSN 2308-1708
Online Edition

ISBN 978-3-7001-9183-4
Online Edition



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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: bestellung.verlag@oeaw.ac.at
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A Comparison of Convolutional Neural Network Architectures for Automated Detection and Identification of Waterfowl in Complex Environments

    Mohammad Mustafa Sa'doun, Christopher D. Lippitt, Gernot Paulus, Karl–Heinrich Anders

GI_Forum 2021, Volume 9, Issue 2, pp. 152-166, 2021/12/28

doi: 10.1553/giscience2021_02_s152

doi: 10.1553/giscience2021_02_s152


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doi:10.1553/giscience2021_02_s152



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: 0xc1aa5572 0x003d25f2

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Rights: https://creativecommons.org/licenses/by-nd/4.0/