Dear Folks,

The study that investigates how human categorize geographic objects in VGI project generally and OSM particularly still need your support

Vist and participate here

Look for results of pilot study, have impression on how objects are recognized differently

Thanks in advance,

Dr. Ahmed Loai Ali

contact me on:loai@uni-bremen.de

Dear All,

… Vêre tot

Dear all,

… Vêre tot

Dear all,

As promised, here are some interesting results of our human-cognitive study after 1 month. The study analyzes how humans are able to describe geographic features in OSM, in particular land-use feature.

Visit the study and participate if you still did not: http://hccvgi.informatik.uni-bremen.de/

Look to some results: http://hccvgi.informatik.uni-bremen.de/reports/index

Enjoy your holidays and Merry Christmas every one.

Dr. Ahmed Loai Ali

Contact:loai@uni-bremen.de

Dear all,

First, we are grateful to all (127) participants in our Study. Some people were great and complete the entire study, Some get bored and did not continue. However, visiting the study and sending us comments or feedback is a great honor for us.

The study is still running under http://hccvgi.informatik.uni-bremen.de/ and needs more participation. Give us 30 min of your precious time and participate. By the end of this week, we will publish some of the initial interesting results.

Thanks again

Bests, Dr. Ahmed Loai, Bremen Spatial Cognition Center (BSCS),

Contact: loai@uni-bremen.de

Dear All,

I have to apologize for that the study link was broken for couple of days. Bugs are already fixed and the study is work well waiting for your contributions. Please, use study http://hccvgi.informatik.uni-bremen.de/ to help science. Our research focuses on YOU (OSM users) and contributors. Participate in the study http://hccvgi.informatik.uni-bremen.de/ to see how perfect you are in recognizing landuse areas.

Dear Folks,

As the year is counting down and ice is in most of the places. Please, use our study http://hccvgi.informatik.uni-bremen.de/ to generate more energy in your brain and consequently your entire body. Participate in the study http://hccvgi.informatik.uni-bremen.de/ to see how perfect you are in recognizing landuse areas.

Dear Folks,

We are computer scientists concerning with analyzing the data quality of your great contributions. To help us, please feel free to participate in our cognitive study. It is simple and takes about 30 min. Your feedbacks are more important for us for improvements. Press here to participate http://hccvgi.informatik.uni-bremen.de/

… Vêre tot

Dear Folks,

We are computer scientists concerning with analyzing the data quality of your great contributions. To help us, please feel free to participate in our cognitive study. It is simple and takes about 30 min. Your feedbacks are more important for us for improvements. Press here to participate http://hccvgi.informatik.uni-bremen.de/

Thank you in advance

Dr. Ahmed Loai Ali loai@informatik.uni-bremen.de

Dear participants,

Thank you for helping science and supporting voluntary geographic contents. The contents which have been exclusively collected by voluntary efforts. In these contents, public with local knowledge is the main source of information. Thus, we are (as a computer scientist) concerning with human cognition influence on data classification. Human perceives things differently.

For example, regarding landuse/landcover classification, we could agree on that a parcel of land is covered by grass or water which is an abstract level of classification. However, whether this parcel is a park, a garden, a cemetery, or even a golf court might be perceived differently. Whether this water body is a pond, a lake or a reservoir likely requires finer measures, geographic knowledge, and particular expertise.

Therefore, we developed a study to understand more about how people perceive geographic contents remotely. The study aims to:

• find out human capabilities to provide landuse data.
• understand the influence of classification mechanism and schema on data quality.
• study human behaviors in providing voluntary data, and
• emphasis challenges of data classification in voluntary contents.

The study takes about 25-30 min. We do not collect any personally identifiable information. Thank you again for your time and your contribution. To participate in the study press here

Don’t hesitate to contact me for further comments and feedbacks.

Best regards,

Dr. Ahmed Loai Ali

loai@informatik.uni-bremen.de

Dear participants,

Thank you for helping science and supporting voluntary geographic contents. The contents which have been exclusively collected by voluntary efforts. In these contents, public with local knowledge is the main source of information. Thus, we are (as a computer scientist) concerning with human cognition influence on data classification. Human perceives things differently.

For example, regarding landuse/landcover classification, we could agree on that a parcel of land is covered by grass or water which is an abstract level of classification. However, whether this parcel is a park, a garden, a cemetery, or even a golf court might be perceived differently. Whether this water body is a pond, a lake or a reservoir likely requires finer measures, geographic knowledge, and particular expertise.

Therefore, we developed a study to understand more about how people perceive geographic contents remotely. The study aims to:

• find out human capabilities to provide landuse data.
• understand the influence of classification mechanism and schema on data quality.
• study human behaviors in providing voluntary data, and
• emphasis challenges of data classification in voluntary contents.

The study takes about 25-30 min. We do not collect any personally identifiable information. Thank you again for your time and your contribution. To participate in the study press here

Don’t hesitate to contact me for further comments and feedbacks.

Best regards,

Dr. Ahmed Loai Ali

loai@informatik.uni-bremen.de

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

… Vêre tot

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

… Vêre tot

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

… Vêre tot

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.

… Vêre tot

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.

… Vêre tot

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.

… Vêre tot

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.

… Vêre tot

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”.

… Vêre tot

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,

… Vêre tot