• Adrijana Car – Thomas Jekel – Josef Strobl – Gerald Griesebner (Eds.)

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

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)

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GI_Forum 2017, Volume 5, Issue 2

ISSN 2308-1708
Online Edition

ISBN 978-3-7001-8251-1
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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OSMDeepOD - Object Detection on Orthophotos with and for VGI

    Samuel Kurath, Raphael Das Gupta, Stefan Keller

GI_Forum 2017, Volume 5, Issue 2, pp. 173-188, 2017/12/13

Journal for Geographic Information Science

doi: 10.1553/giscience2017_02_s173

doi: 10.1553/giscience2017_02_s173


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doi:10.1553/giscience2017_02_s173



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: 0xc1aa5572 0x00373589

Document viewed: Calculating...

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