• Home
  • About

GIScience News Blog

News of Heidelberg University’s GIScience Research Group.

Feed on
Posts
Comments
« GIScience Contributions to the 2019 State of the Map Academic Track proceedings
Colloquium Talk about healthsites.io: Building a baseline of health facility data in OpenStreetMap by M. Herringer »

Detecting OSM Building Facades with Graffiti Artwork Based on Street View Images and Social Media using Deep Learning

Feb 5th, 2020 by GIScienceHD

As a recognized type of art, graffiti is a cultural asset and an important aspect of a city’s aesthetics. As such, graffiti is associated with social and commercial vibrancy and is known to attract tourists. However, positional uncertainty and incompleteness are current issues of open geo-datasets containing graffiti data. In a newly published paper, we present an deep learning approach towards detecting building facades with graffiti artwork based on the automatic interpretation of images from Google Street View (GSV).
It starts with the identification of geo-tagged photos of graffiti artwork posted on the photo sharing media Flickr. GSV images are then extracted from the surroundings of these photos and interpreted by a customized, i.e. transfer learned, convolutional neural network (CNN). The compass heading of the GSV images classified as containing graffiti artwork and the possible positions of their acquisition are considered for scoring building facades according to their potential of containing the artwork observable in the GSV images. More than 36,000 GSV images and 5,000 facades from buildings represented in OpenStreetMap were processed and evaluated for a case study in London (UK).

Precision and recall rates were computed for different facade score thresholds. False-positive errors are caused mostly by advertisements and scribblings on the building facades as well as by movable objects containing graffiti artwork and obstructing the facades. However, considering higher scores as threshold for detecting facades containing graffiti leads to the perfect precision rate.
Our approach can be applied for identifying previously unmapped graffiti artwork and for assisting map contributors interested in the topic. Furthermore, researchers interested on the spatial correlations between graffiti artwork and socio-economic factors can profit from our open-access code and results.

Novack T, Vorbeck L, Lorei H, Zipf A. (2020): Towards Detecting Building Facades with Graffiti Artwork Based on Street View Images. ISPRS International Journal of Geo-Information. 2020; 9(2):98.

Tags: art, convolutional neural networks, deep learning, deep neural networks, Flickr, OSM, social media, streetview images

Posted in OSM, Publications, Research, VGI Group

Comments are closed.

  • About

    GIScience News Blog
    News of Heidelberg University’s GIScience Research Group.
    There are 1,651 Posts and 0 Comments so far.

  • Meta

    • Log in
    • Entries RSS
    • Comments RSS
    • WordPress.org
  • Recent Posts

    • UndercoverEisAgenten on the road
    • Danziger Scholarship Applications Extended to Aug. 31
    • An ohsome way to check if OSM is up to date
    • HeiGIT at MSF Geo Week 2022
    • Open GIScience PostDoc positions on understanding the relationships between “Urban nature experience, biodiversity and mental health”
  • Tags

    3D 3DGEO Big Spatial Data CAP4Access Citizen Science Climate Change Conference crisis mapping Crowdsourcing data quality deep learning disaster DisasterMapping GeoNet.MRN GIScience heigit HOT humanitarian humanitarian mapping Humanitarian OpenStreetMap team intrinsic quality analysis landuse laser scanning Lidar machine-learning Mapathon MapSwipe Missing Maps MissingMaps ohsome ohsome example Open data openrouteservice OpenStreetMap OSM OSM History Analytics Quality quality analysis remote sensing routing social media spatial analysis Teaching VGI Workshop
  • Archives

    • August 2022
    • July 2022
    • June 2022
    • May 2022
    • April 2022
    • March 2022
    • February 2022
    • January 2022
    • December 2021
    • November 2021
    • October 2021
    • September 2021
    • August 2021
    • July 2021
    • June 2021
    • May 2021
    • April 2021
    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
    • October 2014
    • September 2014
    • August 2014
    • July 2014
    • June 2014
    • May 2014
    • April 2014
    • March 2014
    • February 2014
    • January 2014
    • December 2013
    • November 2013
    • October 2013
    • September 2013
    • August 2013
    • July 2013
    • June 2013
    • May 2013
    • April 2013
  •  

    February 2020
    M T W T F S S
    « Jan   Mar »
     12
    3456789
    10111213141516
    17181920212223
    242526272829  
  • Recent Comments

    GIScience News Blog CC by-nc-sa Some Rights Reserved.

    Free WordPress Themes | Fresh WordPress Themes