This document discusses using reverse viewshed analysis to assess the location correctness of geotagged photos from Flickr. The researchers analyzed photos of two landmarks in Berlin - the Brandenburg Gate and Reichstag. They categorized each photo based on whether the geotag and label were correct based on the viewshed analysis and manual inspection. Their results showed geotags and labels were less accurate for photos further from the landmarks and users with fewer total photos. The researchers propose using additional data like observer height and a weighted credibility score to improve location correctness assessment of visually generated VGI.

1. Using Reverse Viewshed Analysis to
Assess the Location Correctness of
Visually Generated VGI
Hansi Senaratne | Arne Broering | Tobias Schreck
2013 ESRI International User Conference - GIScience Session
10.07.2013

2. Volunteered Geographic Information
(VGI)
What is VGI?
• A special case of UGC
• For creating geographic information
• Contributors are quite often untrained
• May or may not be accurate
• Various dedicated Web platforms
• flickr- 6.7 billion images
• OpenStreetMap - 2.75 billion track points
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Use of VGI for disaster relief!

3. Quality of VGI
Hurricane Sandy: a still from the
movie “The Day after Tomorrow”
(Twitter)
Hurricane Sandy: photo-shopped
statue of Liberty with a dramatic
storm hovering over it (Twitter) Reichstag: geotagged 6km East of the
actual location (flickr)
Birkenkopf hill: Overlay of several GPS
tracks (Wikipedia)
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4. E.g. Positional accuracy in
Source: Goodchild & Li. (2012)
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5. E.g. Positional accuracy in
Using metadata to assess the location correctness
and thereby the credibility of flickr contributors
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6. What is Credibility?
The believability of a source or message, which
comprises primarily two dimensions, the
trustworthiness and expertise
Subjective Objective
(Hovland et al. 1953)
(Flanagin & Metzger 2008)
+
Source: http://applemintsoda.wordpress.com/2012/03/19/trustworthiness/
http://www.ianbrodie.com/selling/expertise-driven-selling/
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7. Our Approach
E.g.:
“Brandenburg Gate” ,
“Berlin”
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8. Approach (I). POIs in Berlin
Brandenburg Gate
Reichstag
Sample of 100 photos for each POI
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9. DSM dataset from EuroMaps
• IRS-P5 Cartosat-1 in-flight stereo data (2012)
• Buildings, vegetation
• 5 m post spacing
• relative vertical accuracy of 2.5 m with a linear
error of 90% (LE90).
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10. Approach (II). Reverse Viewshed
Calculation
Source: http://resources.arcgis.com/en/home/
Parameters Default
values
OFFSETA 1
OFFSETB 0
AZIMUTH1 0
AZIMUTH2 360
VERT1 90
VERT2 -90
RADIUS1 0
RADIUS2 Infinity
A reverse viewhsed determines the visibility of a
given target point from many observer points
(Fisher 1996)ESRI UC 2013. 10

11. Approach (III). Categorising Photos
Photo Category Correct Geotag Correct Label
a No No
b No Yes
c Yes No
d Yes Yes
Reverse viewshed  Location correctness
Manual inspection  Label correctness
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12. Approach (IV). Photo Categories for
Brandenburg Gate
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13. Approach (IV). Photo Categories for
Reichstag
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14. What do you think?
• Contributor # 1
– Average tag count per photo: 15
– Contacts count: 1000
– Total photo count: 68,882
• Contributor # 2
– Average tag count per photo: 3
– Contacts count: 13
– Total photo count: 239
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15. … Location correctness of #1 & #2
• Contributor # 1 viewshed
• Contributor # 2 viewshed
Legend
Not Visible
Visible
Legend
Not Visible
Visible
AvgTagCount/photo: 15
ContactsCount: 1000
TotalPhotoCount: 68,882
AvgTagCount/photo: 3
ContactsCount: 13
TotalPhotoCount: 239
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16. Some results we found I
Flickr metadata Brandenburg Gate Reichstag
a(30%) b(19%) c(11%) d(40%) a(27%) b(11%) c(25%) d(37%)
Avg. photo tag
count
18 8 13 11 35 12 22 10
Avg. user photo
count
19,087 3,852 18,354 5,422 8,136 7,928 9,555 2,618
Avg. user contact
count
338 111 134 132 108 141 153 110
Avg. distance to
the target
626.5 402.9 299.1 161.6 1,321 735.9 510.5 436.6
 The further away users are from the POI, the less accurate
they get in geotagging and labeling their photos
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17. Some results we found II
Distance to the target User photo count Photo ta1g7 count
Correct geotag/label Incorrect geotag/label
Correct geotag/label Incorrect geotag/label
Correct geotag/label Incorrect geotag/label

18. Issues to think about!
• Erroneous geotagging using the map interface
• Additional data can improve the viewshed
– E.g. height of the observer from surface point
– Photos taken from higher levels on buildings
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19. Future Work
• Weighted score scheme for Flickr metadata
• User interface for quality aware users
• Credibility of text based VGI
– Twitter credibility assessment
• Based on the information spread
• Based on credibility indicators
i.e., re-tweets, no. of followers,
• ….
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20. Thank You.
Contact: Hansi.Senaratne@uni-konstanz.de
http://infovis.uni-konstanz.de/~senaratne/
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21. BACKUP SLIDES
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22. Quality Assessment of VGI – Related Work
• Distance based TRUST model – Bishr et al. (2008)
• User verification – Goodchild (2009); Coleman (2009)
• Image recognition (flickr) - Friedland (2011)
• Rating systems (GeoLabel) - Lush et al. (2012)
• Proprietary data comparison (OSM) – Haklay (2010); Zielstra et
al.(2010)
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