OpenStreetMap

grass_and_green's diary

Recent diary entries

Reminder of human-centered classification study

Posted by grass_and_green on 11 January 2018 in English (English)

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

HappyNewYear. More Results of land-use human centered classification study

Posted by grass_and_green on 31 December 2017 in English (English)

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

You still have time to participate in 2017, we wish you all Happy New Year

2018

Dr. Ahmed Loai Ali

Postdoctoral researcher @ Bremen Spatial Cognition Center

Loai@uni-bremen.de

Initial Results from study human cognition influence on data classification in OSM

Posted by grass_and_green on 21 December 2017 in English (English)

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

OSM land-use classification cognitive study... Thanking and gratefulness

Posted by grass_and_green on 18 December 2017 in English (English)

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

Could you classify Landuse correctly? Do it

Posted by grass_and_green on 13 December 2017 in English (English)

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.

New week and new energy required

Posted by grass_and_green on 11 December 2017 in English (English)

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.

Study to analyze mental data classification influence on OSM land use data

Posted by grass_and_green on 7 December 2017 in English (English)

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/

Alt text Alt text Alt text Alt text Alt text Alt text

Thank you in advance

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

How do you classify land use on OSM? Do you believe you could do it well?

Posted by grass_and_green on 6 December 2017 in English (English)

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

A user study to understand human cognition influence on data classification quality

Posted by grass_and_green on 25 November 2017 in English (English)

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

A user study to understand human cognition influence on data classification quality

Posted by grass_and_green on 23 November 2017 in English (English)

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

Location: Altstadt, Mitte, Stadtbezirk Bremen-Mitte, Bremen, Free and Hanseatic City of Bremen, 28195, Germany

Grass&Green: with more entities to enhance the classification

Posted by grass_and_green on 9 February 2016 in English (English)

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

Alt text

• It has very supportive and easy to use interface

Alt text

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.
Alt text

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.

Alt text

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"

Alt text

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.

Alt text

Alt text

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.

Towards OSM community revision of data confirming data classification

Posted by grass_and_green on 3 December 2015 in English (English)

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

Alt text

• It has very supportive and easy to use interface

Alt text

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.
Alt text

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.

Alt text

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"

Alt text

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.

Alt text

Alt text

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.

Grass&Green: A customized QA tool for specific geographic features

Posted by grass_and_green on 30 November 2015 in English (English)

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

Alt text

• It has very supportive and easy to use interface

Alt text

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.
Alt text

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.

Alt text

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"

Alt text

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.

Alt text

Alt text

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.

Grass&Green for better grass classification and understanding

Posted by grass_and_green on 16 November 2015 in English (English)

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. Alt text

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. Alt text

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" Alt text

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. Alt text

then, Contribute simply Alt text

Thanks for all previous and coming contributors

Customized QA tool for specific features at specific Country

Posted by grass_and_green on 9 November 2015 in English (English)

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.

Alt text

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.

Alt text

It gives you a full guide of definitions of each feature Alt text

Try Grass&Green, help us to identify which features that should be inherted in a "park", a "forest", a "meadow", or a "garden".

Contribute to improve the classification

Posted by grass_and_green on 23 October 2015 in English (English)

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. Alt text

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. Alt text

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" Alt text

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. Alt text

then, Contribute simply Alt text

Thanks for all previous and coming contributors

Grass&Green

Posted by grass_and_green on 21 October 2015 in English (English)

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. Alt text

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. Alt text

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" Alt text

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. Alt text

then, Contribute simply Alt text

Thanks for all previous and coming contributors

Use Grass&Green and enhance the data classification

Posted by grass_and_green on 12 October 2015 in English (English)

Dear all, Alt text 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 Alt text

and face the challenge of data classification and help us to get an agreement on a conceptual definition for that classes

Alt text

it is a research project and aims directly to enhance the data quality of OSM data?

Best,.

Grass&Green --- grass.and.green@gmail.com

DO NOT FORGET TO SPEND TIME ON Grass&Green

Posted by grass_and_green on 9 October 2015 in English (English)

Dear all,

Alt text 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,

Alt text

Look how much percentage is people agree of partial agree on our classifications.

Alt text

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

Grass&Green is presented in ISSDQ 2015

Posted by grass_and_green on 6 October 2015 in English (English)

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](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,