- freely available
ISPRS Int. J. Geo-Inf. 2016, 5(6), 87; https://doi.org/10.3390/ijgi5060087
2. Related Work
2.1. VGI Quality Assessment
2.2. VGI Quality Enhancement: Approaches and Methods
2.3. Human-Centered Data Classification
3. Beyond Data Classification in VGI Projects: The Case of OpenStreetMap
3.1. Classification by Tags (key = value)
3.2. Subjective Classification
- Incomplete classification: the limited local knowledge of a participant or the unclear perceived observation from the provided satellite images impacts the classification granularity. In a pilot study on the OSM data set of Germany (May 2015), we found 225,933 entities related to water body classes. Only 20% of these entities have finer classes like lake, waste water, etc. We detected about 10,520,418 unclassified building entities, which have a coarser classification as building while other entities of building are classified into finer classes like residential, industrial, etc.
- Inconsistent classification: when participants interpret a given feature differently, they assign it to conflicting classes or an ambiguous class. During our investigations, we found out that some entities are assigned to conflicting classes; some entities are classified as meadow (i.e., grass land) and wetland (i.e., water body). Figure 1 illustrates a clear example of the classification inconsistency, when the given entity is classified by the pitch, school, and beach classes.
3.3. Conceptual Overlapping Classes
4. Rule-Guided Classification Approach
- Data processing:From the OSM data set of Germany, we extracted the entities of target classes. The entities are extracted from the most densely populated cities to ensure data of high quality. We are concerned with the areal entities. Thus, to understand the qualitative characteristics of the classes, we topologically checked each individual entity. We developed an automatic algorithm using the 9-Intersection Model (9IM) to perform the investigation . This investigation aims to find out the common topological relations between pairs of entities; these relations are potentially useful to distinguish between similar classes. For example, find the relation between pairs of entity (, ), when represents the target feature (e.g., park entity) and is another kind of nearby feature to (e.g., playground, water bodies, etc.).
- Learning:The target of the learning phase is developing a classifier able to potentially distinguish between similar classes. We apply an associative classification  data mining mechanism to perform the learning task. This mining approach utilizes the association rule to construct the classification system . First, we extract a set of predictive rules that describe each class, and then these rules were ranked and organized into the classifier. During the classification process, a given entity is matched against the entire extracted set of rules. The matched rules are ranked in descending order based on their confidence measures. Due to the overlapping problem (see Section 3), the developed classifier is configured to give the two most appropriate classes instead of picking out a single class.
- Validation:Due to the nature of VGI, the proposed approach exploits crowdsourcing to validate the classification. The entities are re-classified using the developed classifier. Afterwards, they are presented to the public again for the purpose of revising the recommended classes. The validation phase has multiple functionalities: (a) enhance/ensure the target entities’ classification by crowdsourcing revision; (b) understand the public conception of target classes; and (c) find out the response of participants to the provided recommendations.
5. Grass&Green: Customized Quality Assurance Application
5.1. Application Description
5.2. Application Architecture
5.3. Announcement Methods and Target Participants
- OSM diaries:We announced the launch and the objectives of the application locally to the OSM mappers through the project diaries (https://www.openstreetmap.org/user/grass_and_green/diary). The OSM diaries are public to every one.
- Social Media:We developed two pages for the project: one on Twitter (https://twitter.com/grass_and_green) and the other on Facebook (https://www.facebook.com/grassANDgreen/) to use the power of social media to attract public participants. We infrequently sent news of the application and thanked the participants on the project pages.
- Others:Mailing lists and paper-based flyers are also utilized to target other researchers and students as well.
6.1. Participant and Contribution Patterns
6.2. Participant Responses
- Complete agreement: when a participant agrees with both of the recommended classes and marks them with the “yes” option.
- Partial agreement: when a participant agrees with only one of the recommended classes and marks the other with a “no” or “maybe” option.
- Disagreement: when a participant does not agree with any of the recommended classes and marks them both with a “no” or “maybe” option.
6.3. Enhanced Data Classification Quality
6.4. Participant Feedback
Conflicts of Interest
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|OSM Tag||Class||OSM Tag||Class|
|landuse = grass|
or landcover = grass
|grass||natural = wood|
or wood = yes
|leisure = park||park||natural = water||water|
|leisure = garden||garden||natural = water|
water = lake
|landuse = recreation ground||recreation||natural = water|
water = pond
|landuse = meadow||meadow||natural = water|
water = reflecting_pool
|natural = scrub||scrub||natural = water|
water = reservoir
|natural = grassland||grassland||natural = water|
water = wastewater
|natural = heath||heath||natural = wetland|
wetland = swamp
|landuse = forest||forest||natural = wetland|
wetland = marsh
|Entities/Class Before Validation||Participants’ Response||Previous Class in Recommendation||Previous Class Not in Recommendation||Acceptance Percentage|
|Classes||In Recommended Classes||Participants Response|
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