We are very grateful to those delegates who volunteered to act as rapporteurs and take notes during the Group Discussions. The summary of discussions are written and compiled purely out of community goodwill and are intended to represent the views and opinions discussed within the discussion groups. They are of high value for research discussions. The difficult task of rapporteuring in fast paced discussions is acknowledged. Where possible we have left out specific names of people and companies/organisations.
Source: Flickr image - flipchart page 1
Consider the A4C4 discussed by SwissTopo:
These can be considered as part of the "system". However when we consider the "C" - these are more difficult
Could we teach machine learning algorithms to consider some of the "C" parts? The outputs from machine learning could then be verified by people against the actual mapping work carried out. However this is not an easy task. We will need: sufficient amounts of data, to trust the people doing the mapping, to be able to measure or ensure consistency, and allow for multiple languages in our machine learning algorithms.Source: Flickr image - flipchart page 2
Opportunities here for input from experts in human factors. How do we go about resolving disputes in crowdsourced mapping? What about the signal to noise ratio of the data which is generated?
The best ROI may come from the long tail or peak contributors to a project. With these contributors you might get as good an ROI as involving a very large number of contributors. A small number of very dedicated people can be very effective. But this will always depend on the task(s). Depending on the task(s) it might be necessary to involve large numbers of contributors.
Perhaps the best approach might be to consider small tasks/challenges with some type of reward system in place. This could encourage contributors to map or collect something "new" or "rare" or to "go somewhere new" to map.
Where does Remote Sensing/Satellite Imagery have a role to play? Consider the potential of a daily return of one satellite every day on a continuous basis. There will be new training challenges. However it could also allow for new forms of feedback - VGI could verify the classification of an image (for example)Source: Flickr image - flipchart page 3
Feedback processes do exist within private companies. Feedback is also part of the process in VGI and NMCAs.
Semantics is a common issue between the two sides
VGI and NMCA are actually trying to understand and represent the same "concepts".
This shared problem opens up the opportunity to work around license problems. Errors do not have any particular license! Errors can be considered as shared work
Is the research focus on VGI too narrow at the moment? One could argue that there is a North-West VGI - but is this representative of a Global VGI?
Which application areas or policy areas could be addressed to deliver more innovative solutions if we compliment or combine NMCAs and VGI? This is a very interesting question - but not such an easy one to answer.
Looking forward - we need to find use-cases where VGI and NMCAs can work together on NEW problems. Think about this siutation with the percieved boundaries of NMCAs. Think about the roles of the NMCAs.Source: Flickr image - flipchart page 4
Rather than considering places where NMCAs and VGI are different - are there use-cases where both NMCA and VGI could work together? These use-cases would be of mutual benefit to both sides while at the same time addressing issues which are of value to wider society, government, etc. The following is a listing of the ideas which were suggested - lots of interesting food for thought here - also lots of interesting starting points for collaboration and research.
We decide to separate the general question into two perspectives : NMCA and non-NMCA