OpenStreetMap
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,
Thanks for the large number of participants who contributed to our study. If you keen to see some pilot results. Feel free to visit this page
To know more about the figures, please visit out study pages.
However, we still looking forward to more participants. Please, support our study and participate in the study here
Thanks in advance
Dr. Ahmed Loai Ali
Bremen Spatial Cognition Center, Bremen University, Germany
Dear all,
Since one month, we are running a cognitive study to understand human mental efforts of data classification in OSM and similar voluntary mapping projects.
The study under the following link here. However, you can read more about the [initial results] (http://hccvgi.informatik.uni-bremen.de/reports/index)
You still have time to participate in 2017, we wish you all Happy New Year
Dr. Ahmed Loai Ali
Postdoctoral researcher @ Bremen Spatial Cognition Center
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/
Thank you in advance
Dr. Ahmed Loai Ali loai@informatik.uni-bremen.de
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
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:
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
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:
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
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
• 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.
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
• 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.
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
• 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.
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.
The next was only grass with fence, the contributors with our tool. make it “Graden” which is more expressive information than grass with fence.It was checked by 5 contributors.
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”
Finally, we still need your contributions. It is added to your editing history and scores. Just Visit Grass&Green and login by your OSM account.
then, Contribute simply
Thanks for all previous and coming contributors
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.
It gives you a full guide of definitions of each feature
Try Grass&Green, help us to identify which features that should be inherted in a “park”, a “forest”, a “meadow”, or a “garden”.
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.
The next was only grass with fence, the contributors with our tool. make it “Graden” which is more expressive information than grass with fence.It was checked by 5 contributors.
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”
Finally, we still need your contributions. It is added to your editing history and scores. Just Visit Grass&Green and login by your OSM account.
then, Contribute simply
Thanks for all previous and coming contributors
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.
The next was only grass with fence, the contributors with our tool. make it “Graden” which is more expressive information than grass with fence.It was checked by 5 contributors.
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”
Finally, we still need your contributions. It is added to your editing history and scores. Just Visit Grass&Green and login by your OSM account.
then, Contribute simply
Thanks for all previous and coming contributors
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”.
sign in to Grass&Green
and face the challenge of data classification and help us to get an agreement on a conceptual definition for that classes
it is a research project and aims directly to enhance the data quality of OSM data?
Best,.
Grass&Green — grass.and.green@gmail.com
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,
Look how much percentage is people agree of partial agree on our classifications.
Do your best, Improve the data, Encourage others, Contact us, Follow us on Twitter and Facebook….By these ways we enhance the data quality.
Thanks
Grass&Green