OSM, as a VGI mapping project, inherits various challenges: naive contributors, flexible contribution mechanism, and uncertainty of spatial data. The facts that rise subjective classification problems in the resulting data. Whether a piece of land covered by grass is classified as “park”, “garden”, “meadow”, or “forest”; whether a water body is classified as “pond”, “lake”, or “reservoir”. In OSM project, such of these classification answers are likely dependent on contributors’ perceptions. However, the appropriate classification of entity is strongly related to some qualitative observations and quantitative measures.
Thus, Grass&Green is a tool that has been developed to help contributors to assign the most appropriate classification of some entities of grass-related features. The role of contributors is needed not only in adding new data, but also to revise and confirm the existence one.
• The tool has an access and contribution to users accounts
• It has very supportive and easy to use interface
On the right hand, it presents the entity under investigation with some qualitative descriptions of its context. On the left hand, it show the OSM entity combined with some recommendations and suggestion, with full flexibility to update/confirm/reject these given recommendations.
• The tool provides the user with textual and visual descriptions of the target features and similar identical features.
Example of classification improvements:
o The next was only grass with fence, the contributors with our tool makes it “Garden” which is more expressive information than grass with fence.
o The next was labeled as “Park”, however its characteristics is far from being park for amusements and entertainment. Out tool correct it to the reasonable classification as “Garden”
The status of contributions: We have around 250 participants from more over that 30 counties. They checked around 3000 entities in about 3 months. However, we are seeking for more confirmation and rejections. The data need to be check and we adapt the methodology of crowd-sourcing revision.
They are mostly agree or partial agree with our recommendations and working to improve the classification quality of these features.
So, please feel free to contribute and follow us on Twitter and Facebook. Send us your feedbacks. Every small contribution even for one entity would be appreciated.