Hi mappers,

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

… See full entry

Dear All,

VGI projects, like OSM, inherits various challenges: naive contributors, flexible contribution mechanism, and uncertainty of spatial data. The facts that rise a 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. While arm-chair contributors, at most cases, don’t have the ability to check the contributed entities well.

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

… See full entry

Dear All,

VGI projects, like OSM, inherits various challenges: naive contributors, flexible contribution mechanism, and uncertainty of spatial data. The facts that rise a 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. While arm-chair contributors, at most cases, don’t have the ability to check the contributed entities well.

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

… See full entry

Dear All,

Grass&Green Still improve the classification quality of grass-related features in OSM @ Germany We still need more analysis and feedback from you all to improve the tool and afterwards apply the methodologies in other locations. Here, are some results

It was village_green only. Which is in some location in Germany is not familiar term. Our contributors labeled it as “Park”. It has been checked with 14 contributors.

… See full entry

Dear all,

It is a reminder of the great Quality Assurance tool that works to improve the classification quality of grass features at germany. The tool called Grass&Green and in place since September 2015.

The tool is easy to use and help directly to improve OSM data quality and connected directly to your mapping profile on OSM. It is important for all mappers to spend time not only in putting and editing new data, but also to improve the old ones.

… See full entry

Dear All,

Grass&Green Still improve the classification quality of grass-related features in OSM @ Germany We still need more analysis and feedback from you all to improve the tool and afterwards apply the methodologies in other locations. Here, are some results

It was village_green only. Which is in some location in Germany is not familiar term. Our contributors labeled it as “Park”. It has been checked with 14 contributors.

… See full entry

Dear All,

Grass&Green Still improve the classification quality of grass-related features in OSM @ Germany We still need more analysis and feedback from you all to improve the tool and afterwards apply the methodologies in other locations. Here, are some results

It was village_green only. Which is in some location in Germany is not familiar term. Our contributors labeled it as “Park”. It has been checked with 14 contributors.

… See full entry

Dear all, I liked the idea of OSM to extreme until I do my research in the project, which is really a challenges.

In my research, we focus on the classification of grass-covered entities, “park”, “garden”, “meadow”, “forest”, “wood”, “recreation”, etc.

Do you know the clear definitions for that classes? they are all “grass”.

… See full entry

Dear all,

So far, the results are promising and work received positive feedback in the scientific community. In addition, we received lots of emails, asking for applying that in other countries and places. However, we still need a continuous support and participation from your sides. Visit Grass&Green and contribute. Fun, Challenges, and Active contribution to OSM (increase your profile).

Here we are, look how many people contribute during the last short period of 24 days,

… See full entry

Last week during the geo-spatial week that organized by the International Society for Photogrammetry and Remote Sensing (ISPRS). There exist an International Symposium of Spatial Data Quality (ISSDQ). In that event, a Grass&Green tool is presented during the talk about “TOWARDS RULE-GUIDED CLASSIFICATION FOR VOLUNTEERED GEOGRAPHIC INFORMATION”.

The tool enhance the classification of grassy entity in Openstreetmap. It is a pilot study on the data set of Germany. Please, participate in the study to help into developing a consistent data classifications.

Best,

Visit Grass&Green to improve the classification of grass-related entities. As a start, it now only German data set, however it is a research to generalize the concepts and ensure the feasibility of the methodologies.

The tool and the research would be presented this week in International Symposium of Spatial Data Quality.

Enjoy the tool and improve the data classification

Best

Visit Grass&Green to improve the classification of grass-related entities. As a start, it now only German Dataset, however it is a research to generalize the concepts and ensure the feasibility of the methodologies.

It is very simple QA tool with easy Interface

Login by your OSM account

… See full entry

Hallo Everyone,

Green is everywhere around us. The most noticeable color on the map is the Green: farms, parks, gardens, forests, meadows, etc. Furthermore, the classification of such of these entities plays a major role in many applications: POI search, ecosystems, climate changes, environmental monitoring, etc. At the same time, these entities on OSM are poorly attract investigations (Mostly have 1-5 versions).

Thus, we target these entities classifications by designing QA tool called Grass&Green. Feature-targeted tools is the powerful solution to manage the data quality. E.g, how many tools investigate the street networks and how it is now looks like (perfect). So, contribute a few of your time in QA tools is required and as much important as contribute new data.

Help us to:

• understand how people perceive and use grassy entities
• enrich VGI data and improve data quality
• help the ecosystem for better environmental analysis

Best

Dear all, After 16 days of publishing Grass&Green, a specific tool for QA of data classification, we got the following contributions.

Thanks for all contributors, however, we still have 1000’s of entities needed to be checked. I could describe simply how does it work?

We have the following assumptions:

• Similar entities should be classified consistently, at least within the same country. For example, In Germany, what people know about “park”, as a place for recreation and amusement, doing sport, grilling, pinking, ..etc. Thus, the one can not called a small piece of grass entity in front or backyard of his home as “park”. It is inconsistency.

• OSM in lots of cities (e.g., urban cities) has a good and acceptable quality.

Hence, we extract the characteristics that describe specific grass-related classes like: forest, meadow, park, garden, and grass. Afterwards, we develop a recommendation system (Grass&Green) for that classes.

… See full entry

Dear all, After 16 days of publishing Grass&Green, a specific tool for QA of data classification, we got the following contributions.

Thanks for all contributors, however, we still have 1000’s of entities needed to be checked. I could describe simply how does it work?

We have the following assumptions:

• Similar entities should be classified consistently, at least within the same country. For example, In Germany, what people know about “park”, as a place for recreation and amusement, doing sport, grilling, pinking, ..etc. Thus, the one can not called a small piece of grass entity in front or backyard of his home as “park”. It is inconsistency.

• OSM in lots of cities (e.g., urban cities) has a good and acceptable quality.

Hence, we extract the characteristics that describe specific grass-related classes like: forest, meadow, park, garden, and grass. Afterwards, we develop a recommendation system (Grass&Green) for that classes.

… See full entry

I just would like to thank the contributors and the power of crowds. more than 800 entities checked about 100 users, more than 150 visits to the system. 90% agreement with recommendations and still more entities need your opinions

This entity was classified as ‘park’ by Grass&Green it is confirmed classified as ‘park’

… See full entry

Dear all,

After 12 days of Grass&Green in place. We have around 100 users and contributed in about 500 entities with 90% agreement with our recommendations. However, there is still exist 1000’s of entities to be checked. So, we ask for daily contributions to improve the classification of grass-related features. It is just the start, we would feed the system with more categories of entities later on e.g.: water-related features and other natural features. Here is the interface of the system. Just visit http://opensciencemap.org/quality/ and login by your osm account and contribute to improve the classification quality.

Thanks for all former contributors and useful comments. I appreciate your feedback

Best, Ahmed Loai Ali

After 5 days Grass&Green contributors agree or partial agree to our recommendation by 91.5 %. Dear OSM users hurry and participate in the contribution to the tool. It is a research project. The project has many objectives: 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.

Do you could tag the following image as a park? Absolutely, This tag is inappropriate and the appropriate tag should be grass.

… See full entry

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?

… See full entry

Dear OSM users,

Grass&Green is a research project aims to improve the classification of grass-related entities. Actually, the main aim are 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 research is done 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.

Best,

Ahmed Loai Ali