OpenStreetMap

Dear OSM users,

Grass&Green is a new tool to improve the quality of data from classification perspective.The project aims to: 1) develop an appropriate classification of entities to support more use;2) guide the participants towards better understanding of the class;3) enrich the OSM data; 4) correct the miss classified data.

The given entities in the previous figure. Is is a park? could it be classified as a garden? what is the best classification of that entity? While in the next figure is the entity is a park or forest? could it classified as meadow?

The tool is part of a research at Bremen University by Ahmed Loai Ali. The research argues that the appropriate classification of entities comes from the inherent characteristics of the entities and its geographical context. For example, when an area covered by grass and contains amusements and leisure properties then it is recommended to be classified as park, garden, recreation,..etc. Whereas when an identical entity contains nothing and full will woody plants, then it would be better to classify it as forest. While in other situation when a grass entity located between roundabouts and besides highways and aims to decoration purpose it could be classified as grass. When the entity used for agriculture then its field, farm, ..etc. For further details you could read our research publications.

The tool that we develop focus currently on German data only, and we still analysis the classification within the city boundaries. So, It represents a way to improve our research and our research plan still have more items.

The tool is online under http://opensciencemap.org/quality/. We need to understand how participants see the classification of grass-related entities. We need to check if participants could really able to classify these type of entities correctly from only satellite images and local knowledge. To which extent is our generated recommendations matches with participants' opinion.

Hence, It is a kindly call for participants. Let's improve our data sets, Let's understand various conceptual perspectives. I would appreciate your participation. Your comments and feedback and more than welcome.