GI_Forum 2017, Volume 5, Issue 2 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 2017, Volume 5, Issue 2 Journal for Geographic Information Science
ISSN 2308-1708 Online Edition ISBN 978-3-7001-8251-1 Online Edition
Samuel Kurath,
Raphael Das Gupta,
Stefan Keller
S. 173 - 188 doi:10.1553/giscience2017_02_s173 Verlag der Österreichischen Akademie der Wissenschaften doi:10.1553/giscience2017_02_s173
Abstract: A great deal of the interesting information captured by aerial imagery is as yet unused, even though it could help to enrich maps and improve navigation. For this information to be made available, objects such as buildings or roads need to be recognized on images. This is laborious to do entirely manually, but non-trivial to perform computationally. In this paper, we present an automated method for detecting objects of a chosen class (pedestrian crosswalks) on orthophotos, a method which can be adapted for various classes of objects. The method uses a supervised machine-learning approach with a deep convolutional neural network. We re-trained the final layer of a pre-trained neural network using specific imagery and crowdsourced geographic information from the OpenStreetMap (OSM) project. The result is an easily enhanceable and scalable application which is able to search for objects in aerial imagery. We achieved an accuracy of well over 95% for crosswalks and promising preliminary results for roundabouts. Keywords: visual recognition, deep convolutional neural networks, aerial imagery, VGI, parallelism Published Online: 2017/12/13 12:19:14 Object Identifier: 0xc1aa5576 0x00373589 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 |