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Cross-Linkage Between Mapillary Street Level Photos and OSM Edits

  • Levente JuhászEmail author
  • Hartwig H. Hochmair
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Mapillary is a VGI platform which allows users to contribute crowdsourced street level photographs from all over the world. Due to unique information that can be extracted from street level photographs but not from aerial or satellite imagery, such as the content of road signs, users of other VGI Web 2.0 applications start to utilize Mapillary for collecting and editing data. This study assesses to which extent OpenStreetMap (OSM) feature edits use Mapillary data, based on tag information of added or edited features and changesets. It analyzes how spatial contribution patterns of individual users vary between OSM and Mapillary. A better understanding of cross-linkage patterns between different VGI platforms is important for data quality assessment, since cross-linkage can lead to better quality control of involved data sources.

Keywords

Volunteered geographic information Mapillary Openstreetmap User contributions Cross-linkage 

1 Introduction

In recent years, technological developments in computer, sensor, and communication technology together with an increase in citizen’s interest in sharing spatial information led to a significant growth of crowdsourced geographic information, often referred to as Volunteered Geographic Information (VGI) (Goodchild 2007), which became accessible on Web 2.0 platforms and social media. Contribution patterns for individual VGI applications, such as OpenStreetMap (OSM), photo sharing services, or drone imagery portals, have already been extensively analyzed in the Geoscience literature (Neis and Zielstra 2014; Hochmair and Zielstra 2015; Hollenstein and Purves 2010). However, it is less understood if and how users participate in several crowdsourcing platforms, whether an individual contributor’s activity spaces in different VGI platforms are spatially co-located or spatially distinct, and how data are cross-linked.

Mapillary provides a crowdsourced alternative of street-level photographs to Google Street View. Since its public launch in February 2014 members of this project have so far provided more than 45 million street level photographs along a total of 1,250,000 km of roads and off-road paths. Besides being a crowdsourced and therefore free alternative to Google Street View, Mapillary has also the advantage that its users can take photographs with a smartphone and upload them with an app, without the need of professional camera equipment. This makes Mapillary particularly suitable for image collection on off-road paths, such as hiking or cycling trails. Since street level photographs provide supplemental information to other free alternative data sources, such as aerial photographs, satellite imagery, or census data, they are beginning to be used in other VGI platforms. For example, source tags of OSM edits indicate that Mapillary imagery is already used to edit OSM features. Mapillary image content can be used to identify features or feature attributes that cannot be seen on the aerial imagery but are visible on ground level photos only, such as names of bus stops or buildings. Therefore, Mapillary provides “local” knowledge for OSM remote mappers who do not map in the field. Mapillary imagery is included as a layer option in two of the common OSM editors (iD and JOSM), making it easier for mappers to use street level photos for data editing in OSM.

The overall objective of this study is to determine to which extent Mapillary imagery is currently used for OSM feature editing. More specifically, it aims to determine how the use of street level photos for this purpose varies between different parts of the world, which OSM features are primarily mapped and edited based on Mapillary imagery, how spatially distinct OSM and Mapillary contributions for an individual mapper are, and how cross-linkage to Mapillary is provided in OSM, i.e. which tags are used to reference this connection. This topic is relevant for VGI research since we assume that cross-linkage between different VGI data sources can improve data quality, both by increasing completeness of linked data sources, but also through quality control and review of the original data source, such as the location of an image in the linked platform.

The remainder of this paper is structured as follows: The next section provides a review of related literature, which is followed a description of the study setup. After this, analysis results are presented, which is followed by conclusions and directions for future work.

2 Literature Review

Understanding user contribution patterns is important in the context of spatial data quality of VGI (Budhathoki and Haythornthwaite 2013; Coleman et al. 2009). Therefore numerous studies examined data growth patterns for various crowdsourced geographic data platforms and identified mapper types and mapping communities. Due to the topic of the presented paper the literature review section will focus on community and cross-linkage analysis in previous studies, where OSM is one of the most frequently analyzed VGI platforms. One study analyzed community development in OSM between 12 selected urban areas of the world and found that for cities with lower OSM community member numbers a significant percentage of OSM data contributions (up to 50 %) came from mappers whose main activity area was more than 1000 km away from these particular urban areas (Neis et al. 2013). The interaction between users in OSM was measured for seven selected cities in Europe, the United States, and Australia, by analysis of co-editing patterns in OSM (Mooney and Corcoran 2014). Results showed that high frequency contributors, so called senior mappers, perform large amounts of mapping work on their own but do interact, i.e. edit and update contributions from lower frequency contributors as well. A related study analyzing the OSM editing history for London revealed that there was limited collaboration amongst contributors with a large percentage of objects (35 %) being edited only once or twice (Mooney 2012). An earlier AGILE workshop focused on the activities and interactions which occur during VGI collection, management and dissemination on various VGI platforms (Mooney et al. 2013), including the semantic aspect of the integration of VGI datasets with authoritative spatial datasets. Community analysis among contributors was also conducted for other crowdsourcing and social media platforms. For example, a field experiment on the online encyclopedia Wikipedia showed that informal rewards (e.g. a thumbs-up) increase the incentive to continue contributing only among already highly-productive editors, but lower the retention of less-active contributors (Restivo and van de Rijt 2014). Another study on Wikipedia identified collaboration patterns that are preferable or detrimental for article quality, respectively (Liu and Ram 2011). For example, articles with contribution patterns where all-round editors played a dominant role were often of high quality. Analysis of the network of Twitter users and their followers shows that, although users can connect to people all over the world, the majority of ties from US based users are domestic (Stephens 2015). That is, the Twitter network in the US is spatially constrained and bound by national borders and population density. The connection between the Twitter online social network and the underlying real world geography is also discernable by the fact that 39 % of Twitter ties are shorter than 100 km, i.e. roughly the size of a metropolitan area, and that the number of airline flights emerges as a better predictor of non-local twitter ties than spatial proximity (Takhteyev et al. 2012).

Although contribution patterns and specifically contributor communities within various VGI and social media platforms have been recently discussed, as described above, comparison of contribution patterns of individual users across platforms and data-linkage across platforms has so far not been analyzed in great detail in the literature. Some studies do compare the density and spatial footprints of data contributions between different VGI and social media platforms, such as between Flickr and Twitter (Li et al. 2013), or between several photo sharing services (Antoniou et al. 2010). However, these studies do not analyze individual contributor behavior or discuss how contributions of one data source are related to contributions from another. Recent trends show that linkage of geographic data across different VGI and social media platforms is a real phenomenon. For example, FourSquare/Swarm users use an OSM background layer to add new check-in places. Therefore OSM positional accuracy directly affects the positional accuracy of FourSquare/Swarm venues. Flickr, a prominent photo-sharing service, has about 30,000 photos tagged with OSM objects. These so-called machine tags potentially allow machine algorithms to automatically extract descriptive information from OSM for Flickr photos. Mapillary uses OSM for reverse geocoding as well. That is, for each photo and photo sequence, the name of the corresponding road is determined by the OSM Nominatim geocoder tool which provides descriptive information of the image locations. In turn, Mapillary photos can be used as a source to derive information for OSM mapping purposes.

It has been shown that OSM positional accuracy is better where high-resolution imagery is available (Haklay 2010). Also, data imports to OSM have clear benefits for areas with a smaller contributor base (Zielstra et al. 2013). Since the Mapillary licensing policy allows OSM contributors to derive information from its imagery, it is a valuable source of geographic information for OSM and other VGI platforms. Within its first year, Mapillary reached significant coverage in some selected cities, and even outperformed Google Street View in terms of completeness in some cases (i.e., in some rural areas and on some off-road segments) as of early 2015 (Juhász and Hochmair 2016). This explains why a growing number of OSM users utilize Mapillary data for OSM data editing, which will be more closely analyzed in the remainder of the paper.

3 Study Setup

The study is split into two parts. The first part uses worldwide data and is conducted at the aggregated level to get insight into how OSM contributors cross-tag feature edits and changesets to express the Mapillary source. It analyzes also which OSM primary features (e.g. highways or amenities) are mostly associated with Mapillary, and whether Mapillary information is used to create new features or to edit attributes of existing features. The second part of the study reviews in more detail the mapping behavior of individual users. More specifically it analyses for users that contribute to both platforms to which extent the areas they map in OSM and Mapillary overlap, and whether one of the two VGI platforms is preferred over the other as a mapping platform. This second part of the study is conducted for Europe.

Following the study design, the data retrieval process is also separated into two parts. The first part extracts from an 11 week period all OSM feature editing events and changesets worldwide that are associated with Mapillary according to their tags. The second part identifies, based on user names, users who contributed to both platforms in Europe. For these users, geometries of OSM changesets and edited or added OSM features, as well as Mapillary photo locations are extracted for subsequent comparison.

3.1 Extraction of Mapillary Related OSM Events

For the extraction of Mapillary related OSM events we used OSM diff files. These files contain all changes made to the OSM database over some time period and can be downloaded at different time granularities from the OSM Website in the compressed OsmChange format. In addition to the daily summary of changes in OSM diffs, we considered also OSM changesets. All these data were extracted between August 31, 2015 and November 15, 2015, covering an 11 week period of OSM edits.

We relate to an OSM event as an insertion or modification of an OSM feature that has an explicit reference to Mapillary. Such references are usually source tags, descriptions, comments or URLs. We consider an OSM event Mapillary related if the expression “Mapillary” (or “mapillary”) can be found in a reference. A software tool, which we developed in Python and Bash, starts with downloading a diff file or changeset. After decompressing the file, the tool converts it into the OPL1 format with the Osmosis tool. The resulting text files contain OSM edits in rows. Therefore it is possible to search and filter edits with UNIX grep commands. If a line (event) is associated with Mapillary (i.e. “Mapillary” or “mapillary” keywords can be found in tag names or values), it is inserted into a spatially enabled PostgreSQL table. Node and changeset geometries can be reconstructed from the object itself. However, way geometries were extracted from the OverpassAPI. As a result of this process, separate tables for OSM nodes, OSM ways and OSM changesets were available for analysis. Each table contains the unique OSM ID of the object (node, way, or changeset), username and ID of the OSM user that made the edit, tags in hstore format, and timestamps of the event.

3.2 Extraction of Mapillary and OSM Features from Across-Platform Users

For comparing the spatial editing and contribution behavior between Mapillary and OSM users, users from both platforms were extracted using string matching of usernames. We used a Mapillary database dump of photo sequences that is a suitable representation of the spatial coverage of photo mapping to extract usernames (Juhász and Hochmair 2016). To reduce the chance of extracting two different users who by coincidence share the same user name, only usernames that are longer than 7 characters were considered for this task. Next, it was checked whether the username from the Mapillary database dump exists also in the OSM database using the main API. Since this is not the intended use of the API, we limited our search to 100 matches. Then we reconstructed the OSM editing history of these OSM users using their changesets. A changeset contains the map edits and their bounding area that are submitted by a user to the OSM database, which is typically done on a regular basis to avoid losing completed edits. We limited OSM contributions to the time period after a user signed up to Mapillary, ensuring that both data sources cover the same time range. Since changesets occasionally cover large areas, concealing details about a user’s primary regions of edits we excluded changesets larger than 225 km2. Using an exploratory approach we found that eliminating the upper tail of the area distribution (Fig. 1a) results in a fairly accurate spatial representation of a user’s OSM editing history, for which retained changesets are shown in (Fig. 1b).
Fig. 1

Histogram of filtered changeset areas (a) and selected changesets in Europe colored by user (b)

To spatially match OSM and Mapillary contributions, a 10 by 10 km grid was created for Europe, limited to the region within the dashed boundaries shown in Fig. 1b. For each cell, OSM and Mapillary edits were extracted for all users that were active in that cell. Results were stored in a PostgreSQL table with unique cell IDs, allowing to spatially match user contributions from both sources. Based upon examination of OSM and Mapillary contributions (areas, descriptions and timestamps) we identified one username which clearly did not refer to the same individual (e.g. editing OSM based on local survey while uploading Mapillary photos from a distant country at the same time). This user was removed from the dataset. The final dataset, after limiting OSM contributions to after the Mapillary signup date and the geographic area to Europe, contained 83 individual users who uploaded photos to Mapillary, edited OSM data, and were most likely the same person.

4 Results

4.1 Contribution Patterns for Cross-Tagged OSM Features

4.1.1 Cross-Linkage Between OSM Event Types and Mapillary

In a first step it was analyzed how and to which extent the OSM community uses Mapillary as a source of information. The analysis was conducted for tags in Mapillary related OSM events, i.e. node and way edits, and changesets (see Sect. 3.1), that explicitly mentioned “Mapillary” or “mapillary”. For OSM nodes, 1930 events were identified, consisting of new insertions or edits. These events occurred in connection with 1660 unique OSM nodes and were carried out by 68 unique users. For OSM ways, we found 1694 events relating to 1330 unique features that were edited by 96 individuals. Furthermore, the “Mapillary” or “mapillary” keywords appear in 5110 changesets submitted by 209 mappers. The weekly aggregated number of events is shown in Fig. 2. The number of users editing nodes or ways, or submitting changesets (smaller than 225 km2) with reference to Mapillary, together with the number unique users per week are summarized in Table 1. The table shows for the different weeks also the total number of OSM users who submitted any changes. Among this group, the percentage of OSM users who submitted changes based on Mapillary images is shown in the last column. Values between approximately 0.5 and 0.6 % indicate that the sub community that uses Mapillary images for OSM data contribution is still a small fraction.
Fig. 2

Number of weekly OSM events cross-tagged to Mapillary

Table 1

Weekly aggregated number of OSM users

Week

Users associated with Mapillary

All OSM users

% OSM users (Mapillary)

Node

Way

Changeset

Unique

Aug 31–Sept 6 (36)

10

12

55

67

11,701

0.63

Sept 7–Sept 13 (37)

12

14

52

63

10,918

0.58

Sept 14–Sept 20 (38)

13

11

47

56

10,476

0.53

Sept 21–Sept 27 (39)

10

14

40

55

10,298

0.53

Sept 28–Oct 4 (40)

9

9

38

49

10,108

0.48

Oct 5–Oct 11 (41)

8

17

56

66

10,606

0.62

Oct 12–Oct 18 (42)

9

13

48

59

10,270

0.57

Oct 19–Oct 25 (43)

18

19

51

68

10,607

0.64

Oct 26–Nov 1 (44)

17

20

52

69

10,872

0.63

Nov 2–Nov 8 (45)

14

13

47

58

11,185

0.52

Nov 9–Nov 15 (46)

7

15

41

54

11,305

0.48

To avoid storing redundant information, OSM users oftentimes attach source information to the changeset rather than to each individual feature. This approach is also recommended when editing multiple features in a mapping campaign. This explains the higher number of committed changesets and the higher number of changeset users with a reference to Mapillary compared to users associated with feature edits. However, it should be noted that not all edits in such tagged changesets are necessarily based on Mapillary alone, although the “Mapillary” or “mapillary” terms appear in the tag. For example, one changeset had a source tag value“bing”, referring to the available Bing imagery, accompanied by several comments, including “Added crossing from Mapillary and bing” or “Sidewalk + surfaces etc. from bing, mapillary and local knowledge”. Analysis of changeset source tags revealed that 29 % of identified changesets” rely solely on Mapillary, local knowledge and surveys, without indicating any other available sources, such as Bing or Mapbox imagery in OSM source tags. We checked also whether Mapillary images overlapped with cross-tagged OSM changesets and found that only 5 % of these changesets were more than 50 m away from the nearest available Mapillary imagery. 84 % of these changesets not located in the proximity of Mapillary imagery were created by the JOSM editor which does not reset the source tag when submitting a new changeset. Therefore these occurrences may be the result of this editor feature, and not of deliberately provided source information by the user. At least one changeset discussion confirms this.2

The spatial distribution of events (individual nodes, ways and changesets combined) is shown in the world map in Fig. 3. Table 2 summarizes relative frequencies of event counts by continent together with user numbers. The map shows that in all regions where Mapillary is mostly contributed, i.e. in Europe and the United States, Mapillary is frequently used as a data source for OSM edits as well. This is also confirmed by user numbers in Table 2, which are higher for Europe and the United States (as part of North and Central America) than for other continents. Table 2 reveals that over 61 % of all node edits and over 44 % of all way edits in OSM during the analyzed 11 week period occurred in Asia, which is surprisingly high given that the share of mapped tracks in Mapillary in Asia from all world contributions is only 4 % as of the beginning of 2015 (Juhász and Hochmair 2016). However, the user numbers for node and way edits in Asia are still much lower than those for Europe, which means that this pattern stems from a relative small group of OSM mappers that apply a source tag to edited individual OSM features rather than to changesets. Contributions to North and Central America show that only very few mappers tag individually edited features (nodes, ways), but primarily tag changesets. OSM events cross-linked to Mapillary occur in all five continents. It should also be noted that the sum of users over aggregated continent data is greater than the number of users extracted from all nodes or changesets, which implies cross-continent mapping activities.
Fig. 3

Spatial distribution of identified OSM events with reference to Mapillary

Table 2

Identified OSM events with reference to Mapillary by continent

Continent

Nodes

Ways

Changesets

Event (%)

Users

Event (%)

Users

Event (%)

Users

Africa

0.05

1

0.00

0

0.08

1

Asia

61.63

11

44.29

16

8.59

19

Europe

37.28

48

50.43

66

45.08

139

North and Central America

0.73

7

4.91

11

43.51

38

Australia and Oceania

0.16

2

0.25

1

0.50

5

South America

0.16

1

0.12

2

2.24

15

Total

100

70

100

96

100

217

An analysis of tag distribution for OSM nodes and ways referencing Mapillary shows that the 10 most common tags, including the “source” tag, represent 60.4 % of all tag occurrences. These can be described as power tags (Peters and Stock 2010; Vandecasteele and Devillers 2015), i.e. tags used frequently by many users. The most common tag was “source”, which was attached to 1507 nodes and 1285 ways.

4.1.2 Cross-Linkage for OSM Primary Features

The next step of the analysis examined the distribution of cross-linkages to Mapillary for OSM primary feature categories. For ways and nodes, features from 21 out of the 26 primary feature categories showed a reference to Mapillary in our dataset. Missing primary features are aerialway, boundary, craft, military, and office. Table 3 shows the most frequently used OSM primary features that were cross-linked to Mapillary. The tags of these OSM features show a clearly different frequency distribution than that of the complete set of OSM features, which was extracted from OSM Taginfo.3 As an example, for node events OSM amenity features are frequently derived from Mapillary (44 %) as opposed to only around 5 % of amenity features that are present in the entire OSM dataset. For way events highway, leisure and barrier OSM features referenced to Mapillary occur at a higher relative frequency than this is the case for the corresponding primary features in the entire OSM dataset.
Table 3

Distribution of primary features cross-linked to Mapillary

 

OSM features referencing Mapillary

All OSM features

Nodes

#

(%)

(%)

Amenity

736

44.34

5.06

Natural

199

11.99

6.34

Highway

174

10.48

6.20

Tourism

102

6.14

0.81

Barrier

83

5.00

1.45

Leisure

74

4.46

0.31

Public transport

47

2.83

0.74

Ways

Highway

620

46.62

28.07

Leisure

239

17.97

0.82

Barrier

194

14.59

1.27

Landuse

76

5.71

4.42

Amenity

50

3.76

1.06

Emergency

49

3.68

0.02

In addition to primary features, 64 OSM features with a key “traffic_sign” that are cross-tagged with Mapillary (3.86 % of nodes) were also found. This de facto tag is also related to transportation and often used outside the “highway =*” tagging scheme. With Mapillary extracting and displaying traffic signs on their website, it is convenient to map traffic signs in OSM.

Surprisingly, some aeroway features, which fall into the category to map air travel related features, appeared in the OSM event list. Although this is outside the focus of typical street level imagery, the flexibility of Mapillary allows users to take and upload photos from virtually anywhere. As a result of this, some airport taxiways have been mapped on the London Heathrow airport based on the imagery (Fig. 4a). Another innovative use of Mapillary that can be seen in the analyzed dataset is indoor mapping. Since it is not possible to obtain GPS coordinates inside a building, postprocessing of images allows users to geolocate their imagery and upload it to Mapillary. The presence of an additional “indoor” OSM tag and negative “layer” and “level” values indicate object positioning through Mapillary indoor-imagery (Fig. 4b). In fact, 191 nodes and 161 ways were tagged as indoor or below surface features. For better integration of Mapillary images into the OSM tagging scheme, a new key called “mapillary” has also been introduced to the OSM community, which allows mappers to reference the corresponding Mapillary image in the OSM feature key. A new initiative, OneLevelUp, already renders this information to a web map. Users also tend to use namespaces, indicating from which direction a Mapillary photo shows the object in question (e.g. “mapillary:NE”). In addition, street level imagery provides the ability to capture descriptive information of features, such as the name of a business (Fig. 4c), the surface type of a road or the material of street furniture. Furthermore, the crowdsourced nature of Mapillary and the ability to capture the rapidly changing world is sometimes a helpful source to obtain an update on geometry information, such as on a modified road layout (Fig. 4d). Interestingly, OSM features highlighted in Fig. 4d do not have a source tag indicating Mapillary, but the following note assigned to them: “PLEASE DO NOT EDIT if you don’t live here. Roads have been completely reconfigured. High-zoom-level imagery is out-of-date (low zoom level imagery is correct). Consult Mapillary.com sequences for this area to see correct road configuration”.
Fig. 4

Using street level imagery in OSM: Mapping runway features (a), indoor mapping (b), deriving descriptive information (c), and deriving new road pattern (d)

4.1.3 OSM Activity Types Associated with Mapillary

In another step the version numbers for edits of individually edited features in OSM that were cross-linked to Mapillary were extracted. This provides information about whether features were newly created (version number 1) or modified (version number >1). A summary of these activity types is provided in Table 4. The large number of edits with a Mapillary reference (last three columns) suggests that street level imagery is used not only to create new features but also to edit existing ones (e.g. to add descriptive information). The table distinguishes between edits applied to nodes that were created during the 11 week analysis period based on Mapillary (left part), and edits applied to nodes that were created before that period or without reference to Mapillary (right part).
Table 4

Number of OSM features based on activity type

 

Created during data collection with Mapillary reference

Edited (created earlier or created without Mapillary reference)

Total

Not edited further

Edited once

Edited more than once

Nodes

692

596

65

31

968

Ways

681

593

74

14

649

4.2 Across-Platform User Contributions

For analysis of individual mapping behavior across the two VGI platforms we extracted Mapillary and OSM contributions of 83 individual users identified earlier as described in Sect. 3.2. This analysis was conducted for Europe (see Fig. 1b). To analyze whether mapped areas of edits are co-located or spatially distinct, for each user, the percentage of 10 by 10 km tiles mapped only in OSM, mapped only in Mapillary, or mapped in both platforms was computed. Results showed that 93 % of users mapped at least some areas in both platforms, resulting in an overlap (Fig. 5a). Even though the sampling of users analyzed for this part of the study started with extracting users from Mapillary, the diagram shows that the majority of users focuses more on OSM (blue area) than on Mapillary (green area) in their data collection efforts. For five users, the exact same tiles are mapped both in OSM and Mapillary. Figure 5b highlights the spatial differences for a selected user in Northeastern Germany, showing that urban areas tend to be mapped both in OSM and Mapillary, whereas rural areas are predominantly mapped in OSM only. The latter may change once urban areas become more completely mapped and thus saturated in Mapillary, so that mappers need to divert more towards rural areas for additional Mapillary contributions. Areas of Mapillary-only contributions can be found along selected major roads (e.g. highway bypass of Berlin). This bypass was already mapped in OSM, but provided a novel contribution option to Mapillary. Mapillary requires users to be physically present at mapping locations, while editing OSM remotely is a common practice. This might be a reason behind OSM contributions being more spatially spread for this user.
Fig. 5

Ratio of mapped areas in Mapillary and in OSM (a) and spatial distribution of a user’s contributions (b)

Curves in Fig. 6 show which percentage of users mapped at least a given percentage of the total mapped area (constructed from OSM and Mapillary tiles combined) in OSM, Mapillary, or in both. For example, the leftmost values mean that 100 % of the users mapped (at least) 0 % of the area in OSM, Mapillary or both. Moving further to the right, one can see that 75 % of the users mapped OSM in at least 50 % of their combined areas, that 48 % of users mapped Mapillary in at least 50 % of their areas, but that only 10 % of users mapped at least 50 % of overlapping areas, i.e. in OSM and Mapillary. Furthermore the diagram shows that 2 % of users have a 100 % overlap in mapped areas.
Fig. 6

Distribution of users by mapped area

5 Discussion and Conclusions

The first part of the study analyzed how Mapillary street level photographs are incorporated and cross-linked in OSM by matching the Mapillary keyword to tags in OSM edits and changesets. Results showed that even during a short period of time (August 31–November 15, 2015), Mapillary images have been used to edit OSM features. It was found that overall Mapillary is most frequently associated with changesets rather than with individually edited features, although the share of OSM events (nodes, ways, changesets) that are cross-tagged with Mapillary varies between the continents. The predominant tagging of changesets might be the result of batch changes in order to avoid the tagging of redundant information with individual features.

The geographic focus of Mapillary related OSM events corresponds to the core areas of Mapillary contributions, which are Europe and the United States. However, due to some local mapping activities, peaks in Japan and in Southeast Asia could also be identified.

The frequency distribution of cross-linked OSM primary features with a reference to Mapillary is significantly different from that of the entire OSM dataset. The percentage of cross-linked features compared to the entire OSM dataset is higher for transportation (highway, public transport, traffic sign) and leisure (natural, amenity, tourism). This finding is in line with common activities associated with Mapillary, which are recording photos while commuting, traveling, and outdoor and leisure activities, such as hiking. The crowdsource nature of Mapillary allows users to map OSM features in places where they are currently less frequently found, including airport taxiways or indoor objects. Cross-linking the two data sources can also help to improve data quality. An example was given where a changed road network pattern was reflected in Mapillary photographs, which were then used to update OSM road geometries. Furthermore the Mapillary images provide a potential data source for adding OSM feature attribute information (e.g. surface type, name of business) without the need to conduct a field survey.

The second part of the study extracted areas of mapping activities from individual users who contributed both to OSM and Mapillary. The analysis revealed that an individual mapper is more likely to edit larger areas in OSM than in Mapillary. Despite this fact it could be observed that 93 % of users in our sample mapped at least some areas that overlapped between OSM and Mapillary. The overlapping areas tend to be located in locations where a user conducts frequent edits, for example in urban areas the user is familiar with.

For future analysis, we plan to extend our data collection methods to include the geographic areas of API calls from the iD and JOSM editors. These areas will reveal the spatial extent for which OSM users loaded Mapillary imagery into the editors. This will allow us to add a temporal component to the analysis, namely to check whether the viewing of the Mapillary street level photos coincides temporally with an OSM event, e.g. node edit, for an area of interest.

Footnotes

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Geomatics ProgramUniversity of FloridaFort LauderdaleUSA

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