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How do volunteer mappers use crowdsourced Mapillary street level images to enrich OpenStreetMap?


Abstract and Figures

An increasing number of crowdsourced geo-data repositories and their services allows volunteer mappers to utilize information from various data sources when contributing data to a crowd-sourced mapping platform. This study explores to which extent OpenStreetMap (OSM) contributors use the crowdsourced street level photo service Mapillary to derive mappable data for OSM during their editing sessions in the iD and JOSM editors. We cross-check the location of OSM edits with the geographic areas from which OSM contributors loaded Mapillary images into the editors to determine which OSM edits could have been based on information from Mapillary images. The findings suggest that OSM mappers are beginning to utilize information from street level images in their mapping workflow. This observed “cross-viewing” pattern between different datasets indicates that the use of data from one VGI platform to enhance that of another is a real phenomenon, leading to implications for VGI data quality.
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1 Introduction
Volunteered Geographic Information (VGI) (Goodchild,
2007) has been recognized as a valuable resource for the GI
community to complement data from traditional sources, such
as the census or aerial photographs. To assess the quality of
heterogeneous VGI sources, studying contributor behaviour is
essential (Elwood et al., 2012, Budhathoki and
Haythornthwaite, 2013). (Bégin et al., 2013) incorporated
editing sessions (OSM changesets) in their analysis to better
understand characteristics and quality of collected VGI.
Results show that new changesets of a contributor usually
extend or overlap spatially earlier changesets and add lower
priority features or new attributes.
OSM is arguably the most widely studied VGI platform. It
was shown that OSM positional accuracy is better when more
mappers edit the same area (Haklay et al., 2010) and that users
are more likely to edit a greater variety of features in their
home region than in external regions (Zielstra et al., 2014).
Whereas VGI mappers rely primarily on their local
knowledge for data contribution and editing, incorporating
other data sources can improve data quality. Examples include
tracing of features from high resolution aerial imagery
(Haklay, 2010) or importing governmental data (Zielstra et al.,
Mapillary’s crowdsourced street level imagery is a unique
addition to the list of available VGI sources. With the
introduction of Mapillary in 2014 and the open license that it
provides, OSM contributors can now use Mapillary image
content to derive information that is not visible on aerial
imagery (e.g. the type of a traffic sign) or to map features that
would require in person exploration through field surveys
(Juhász and Hochmair, 2016a). Evidence of OSM contributors
that use Mapillary imagery to derive feature information was
found by analyzing OSM edits that reference Mapillary in
their tags (typically the source tags), which is referred to as
cross-tagging (Juhász and Hochmair, 2016b). However, as
tagging in OSM is inconsistent and contributors often follow
tagging suggestions only poorly (Davidovic et al., 2016), any
crowd-sourced data sets that were used for OSM edits (e.g.
Mapillary, Flickr) may not be completely referenced in OSM
tag content, calling for alternative methods to identify data use
across different VGI platforms in data editing sessions.
This paper analyzes the viewing extents of the Mapillary
image layer during OSM editing sessions with the iD and
JOSM editors to estimate to what extent Mapillary images
were likely used as a source for OSM edits.
2 Materials and methods
This section provides an overview of the data sources and the
data processing methods used in this research. The goal of the
data extraction was to identify individual OSM feature edits
around the world that were likely based on Mapillary photos.
Such edits would have to be made in the geographic area
where and around the time when the user viewed the
Mapillary image layer in one of the OSM editors.
2.1 Data sources
2.1.1 OpenStreetMap
Since this research studies the editing behavior of volunteer
mappers, a full OSM history dump
was used which includes
How do volunteer mappers use crowdsourced Mapillary street level
images to enrich OpenStreetMap?
Levente Juhász
University of Florida
3205 College Ave.
Ft. Lauderdale, FL, USA
Hartwig H. Hochmair
University of Florida
3205 College Ave.
Ft. Lauderdale, FL, USA
An increasing number of crowdsourced geo-data repositories and their services allows volunteer mappers to utilize information from
various data sources when contributing data to a crowd-sourced mapping platform. This study explores to which extent OpenStreetMap
(OSM) contributors use the crowdsourced street level photo service Mapillary to derive mappable data for OSM during their editing
sessions in the iD and JOSM editors. We cross-check the location of OSM edits with the geographic areas from which OSM contributors
loaded Mapillary images into the editors to determine which OSM edits could have been based on information from Mapillary images. The
findings suggest that OSM mappers are beginning to utilize information from street level images in their mapping workflow. This observed
“cross-viewing” pattern between different datasets indicates that the use of data from one VGI platform to enhance that of another is a real
phenomenon, leading to implications for VGI data quality.
Keywords: OpenStreetMap, Mapillary, VGI, data quality, cross-viewing, contribution behavior.
all historical edits ever made to the database. Due to the large
data volume, the pbf file was first split into world regions and
then imported to a spatially enabled PostgreSQL database,
using the osm-history-splitter and osm-history-importer tools.
2.1.2 Mapillary
Recent versions of the iD and JOSM editors are capable of
loading Mapillary images into their editing environments.
These requests which originate from the editors eventually
leave footprints on Mapillary servers which can be expressed
as a geographic area corresponding to the viewing extent of
the editor. Mapillary provided us with a data dump of all
viewing requests of the Mapillary layer together with their
spatial extents and time stamps. For this analysis we used
worldwide Mapillary viewing extent data that was collected
between June 2015 and February 2016. In addition to this,
another Mapillary data dump with individual photo locations
was used to exclude OSM edits that are far from street level
imagery and therefore probably not based on Mapillary
imagery. Mapillary viewing and photo location data was
stored in the same PostgreSQL database.
2.2 Data preparation and processing
2.2.1 Workflow
The size of a full OSM history dump of over 50 GB in the pbf
format as well as millions of Mapillary viewing extents made
it necessary to split the database into smaller tables
corresponding to world regions. Also, custom indexes were
constructed to speed up data extraction with SQL queries. The
final database contained more than 5 billion rows (OSM edits
and Mapillary viewing extents) and occupied approximately
1.7 TB of disk space.
Based on this customized database structure, a two-tiered
data extraction approach was applied. The first step involved
the extraction of OSM candidate features through a coarse
spatio-temporal match between image viewing windows and
OSM edits (Section 2.2.2 2.2.3). This reduced the database
size for the second step, which involved more refined spatio-
temporal overlay operations (Section 2.2.4).
2.2.2 Extraction of editing sessions
We use the term “editing sessionto describe an uninterrupted
time period within which Mapillary images are being loaded
into the OSM editor from the same machine as part of the
layer viewing request. Since the server-logged viewing data
does not contain a unique OSM user identifier, we used the IP
addresses associated with each request to aggregate the
viewing data over a set time period. More specifically, for
each IP address, a timeline was constructed that shows time
stamps of user activities on the Mapillary layer, such as
changing the viewing extent. An arbitrary one-hour threshold
of idle time (i.e. no image requests) was used to construct
separate editing sessions from the timeline. This one-hour
threshold corresponds to the time period after which the OSM
API closes changesets
if no more edits are made. The
example in Figure 1 illustrates how numerous individual
Mapillary layer viewing extents (yellow rectangles) were
aggregated into two distinct editing sessions (blue polygons
with hatched areas). These editing sessions have start and end
timestamps and a geographic area that can be disjoint (which
is not in the provided examples, though). This aggregation
allows to reduce data volume without losing information
about the editing activity.
Figure 1: Editing sessions (blue hatched polygons) aggregated
from individual viewing extents (yellow rectangles)
2.2.3 Extraction of candidate features
Candidate features are OSM editing events (i.e., creation,
modification) in the spatial and temporal proximity of
Mapillary editing sessions. Since topological operations and
comparison of event timestamps are resource intensive,
identification of a coarse set of candidate features uses the
spatial and temporal index constructed in the PostgreSQL
database. That is, instead of checking the specific spatial
relations between OSM editing events and Mapillary editing
sessions, the database was instructed to utilize related spatial
indexes to determine a potential spatial overlap between
candidate features and Mapillary editing sessions. Similarly,
instead of comparing specific timestamps (with the precision
of milliseconds), an index built on the day of editing events
was used to identify OSM editing events and editing sessions
taking place on the same day. The extraction of candidate
features uses therefore a coarse comparison of spatial extents
and event times, which results in an overestimation of
candidate features compared to the number of actual potential
OSM edits based on Mapillary images.
2.2.4 Extraction of OSM edits likely based on Mapillary
Next, a more refined filtering method was applied on
candidate features for data extraction. Within this step, only
those OSM editing events were retained for further analysis
that were conducted after the start time of a Mapillary editing
session and that were completed within one hour of the
session end time. Since submission of an OSM changeset is
not automated (i.e. users need to send their changes in a
separate step), this threshold is to allow some time in case
users turned off the Mapillary layer before submitting their
OSM changesets. In addition, candidate features further than
25m from the actual location of Mapillary photos were
excluded. Figure 2 highlights the results of this filtering
process. It shows an OSM edit that is considered to be
Mapillary related
(yellow line) as well as other OSM
candidate features (red lines) along with the location of
Mapillary street level photos (green dots). In this example, the
retained edit denotes a new highway exit added to OSM. The
remaining candidate features overlapping with this session
shown in red were excluded from the result set because they
were either further than 25m from the imagery (see (1)) or
they were added to OSM at a time that did not align with the
editing session (see (2)).
Figure 2: Retained OSM edit based on Mapillary (yellow),
excluded candidate features (red), and location of Mapillary
photos (green dots)
3 Results
A total of 34,000 Mapillary editing sessions were identified
between June 2015 and February 2016 out of which 8400
contained only a single Mapillary viewing request. The latter
means either that (1) the user accidentally turned on the
Mapillary imagery layer in the OSM editor, or (2) that there
were no images available for that area so that the user turned
off the layer immediately. Editing sessions with only request
were therefore excluded from further analysis. A Mapillary
viewing session lasted for 7 minutes and 39 seconds on
average. This is the duration users spent on mapping OSM
features while viewing Mapillary street level photos in the
OSM editor. The longest observed session lasted for 5 hours
and covered a large area along a highway in Belgium.
The popularity of Mapillary images used in the OSM
editing workflow can be assessed from the number of editing
sessions per week. Figure 3a shows this information for both
analysed editors. As can be seen, at the beginning of the study
period (June-July 2015), the Mapillary imagery layer was only
accessibly within the iD editor. It became available in the
JOSM editor in August 2015 as well. The average number of
sessions peer week was 283 for iD and 441 for JOSM (after it
became available).
With the extraction of OSM edits based on spatial and
temporal constraints described in Section 2.2.4, the number of
OSM edits per week for both editors can be computed as well
(Figure 3b). The figure illustrates the higher popularity of the
JOSM editor compared to iD when it comes to Mapillary
image use for OSM edits. On average, 400 feature weekly
edits originated from the iD editor as opposed to 4100 coming
from JOSM. The clear preference for JOSM over iD was not
expected, given that ̶ at least ̶ Novice users use iD more
frequently than JOSM to edit OSM data (Yang et al., 2016).
During the most active week (starting on January 4, 2016)
almost 10,000 OSM map edits were identified.
Figure 4 plots the number of different OSM users who use
the Mapillary layer function for OSM feature edits within a
given week. The number clearly increases after the layer
functionality became available in JOSM in August 2015. 980
unique users were found to contribute to OSM based on
Mapillary layer views during the study period.
Figure 3: Number of OSM-Mapillary editing sessions per week, grouped by editor (a), and number of unique OSM mappers
engaging in photo-mapping per week (b)
To analyze the level of experience of OSM users who use the
Mapillary layer service for feature editing, the sign up dates of
these users were extracted from the main OSM API. Figure 4
shows the weekly number of analyzed OSM users by signup
date. The bar chart suggests that novel users are quite active in
utilizing the Mapillary layer feature. On average, 14% of
weekly active users signed up to OSM within just six months
before their editing activity. The proportion of weekly novice
users ranged from 5% to 29%. When setting this limit to one
month before editing based on Mapillary photos, this weekly
rate is still 8% on average. More detailed analysis shows that
almost 30% of all analyzed users created their OSM accounts
after the introduction of Mapillary in 2014.
Figure 4: Weekly aggregated number of photo-mapping OSM
The histogram of user sign up dates to OSM supports this
general finding (Figure 5). A clear peak of new users signing
up at the beginning of 2015 suggests that photo-mapping does
not require one to be overly experienced with OSM. This peak
could be the result of a special promotion activity conducted
by Mapillary. Several meetups were organized to introduce
Mapillary to wider audiences where Mapillary team members
were present in multiple conferences and community events to
promote the service. These promotions might have triggered a
new crowd of mappers to sign up to OSM and to start with
mapping from street level imagery information shortly after
creating their OSM and Mapillary accounts.
4 Discussion and future work
This paper examined to which extent OSM mappers use
Mapillary imagery in their editing workflow. We used the
viewing extents of Mapillary image requests submitted by the
iD and JOSM editors, which provides a unique opportunity to
study mapping behavior. These so-called editing sessions
were spatio-temporally matched with a full history dump of
the OSM database to extract those OSM edits that could be
based on street level photos.
Although weekly counts of OSM feature edits based on
Mapillary images are low compared to the number of all OSM
feature edits submitted per week, our findings indicate that
there is a certain group of OSM mappers who “cross-view”
different VGI data sources for mapping purposes and, more
specifically, use the crowdsourced Mapillary imagery service
to do so. Studying the sign up date of those mappers who
engage in this activity also indicate that, although this process
is more complex than just drawing lines on top of aerial
imagery, novice OSM mappers use Mapillary information,
too, and provide valuable contributions by connecting these
two VGI sources.
Our database contains more detailed information about the
type of Mapillary related OSM edits, such as name changes.
Therefore, we plan to extend the analysis to study what kind
of information has been obtained from street level photos for
subsequent OSM edits.
Figure 5: Histogram of sign up dates of OSM users engaging
in photo-mapping
Since data quality is one of the most important aspects of VGI
analysis, we plan for future work also to determine which
improvements in VGI data quality can be associated with the
re-use of VGI between multiple platforms.
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... A custom workflow was developed and explained in a detailed tutorial (Figure 1b). The workflow uses the JOSM editor since it can load multiple datasets and since it provides superior tools for data editing compared to the Web based iD editor [25]. The tutorial used screenshots, explanations and specific instructions detailing how to execute the import steps. ...
... There is another way for users to contribute to the import task without showing up in the TM or in the history dump. Namely, users could indicate the import process on the changeset level without marking individual features [25,26]. Our TM instance was configured so that the JOSM editor automatically populated the changeset comment field with the #miabuildings hashtag, which makes it possible to query these edits later. ...
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