1. The power of crowdsourcing?
Steffen Fritz, Myroslava Lesiv,Linda See, Ian McCallum, Juan-Carlos, Christoph Perger, Dmitry Schepaschenko, Anatoly Shvidenko, Inian Moorthy, Carl Salk, Martina Duerauer, Mathias Karner, Tobias Sturn, Christopher Dresel, Dahlia Domian, Antonia Dunwoody, Olha Danylo, Juan-Carlos Laso Bayas
Earth Observations Group
Ecosystems Service and Management (ESM)

2. Lessons learnt from IIASA’s crowdsourcing campaigns
Incentives are critical which motivate people to take part in crowdsourcing campaigns (personal recognition, feeling to contribute to a global public good, money, prices, deriving a service or benefit or extra skill)
Quality is critical – what mechanisms can be implemented to improve accuracy (e.g. multiple observations, weighting of contributors according to their geographic location, performance, training, feedback loops)
Training can be achieved via real time feedback

3. Lessons learnt from IIASA’s crowdsourcing campaigns
Communication with the crows is essential
Community building can help to maintain retention rates
Tasks should be entertaining/fun

4. How can we make optimal use of the crowd?
Treated Crowd
Expert
Crowd
Increasing Quality
Another important research question is how can we make optimal use of the crowdsourced data. Closely related to that question is what is the quality of the crowdsourced data. In order to assess the quality we compare the crowdsourced data with the official data to be assessed. We will compare the crowdsourced data with the official data and evaluate the quality. We will develop algorithms how to make optimal use of the crowd, how bias can be corrected and possibly how exeptionally well performing individuals can be identified. We will furthermore monitor if and to what degree the quality of the crowd increases over time. Treated Crowd:
Algorithms
Bias correction
Identification of
‘super experts’
- Learning

5. Cropland Capture 5 million obervations

6. Picture Pile 4 million observations Deforestation Pile Pending:
Cropland
Human Impact
Oil palm identification
 New scoring mechanism
More reference data based on expert classifications

7. FotoQuest Go
FotoQuest Austria

8. LUCAS
photographs

9. FotoQuest Austria – FotoQuest GO
total
July, 2015
September, 2016
300+
Players
4100+
Quests
22,000+
Photos
25%
Quests completed

10. SIGMA Campaign: CROPLAND VALIDATION
Duration: 3 weeks
Number of participants: 92
Number of validations:
Number of observations per site: 5
Check our video:
Join us on Facebook:
https: //

11. SIGMA Campaign: CROPLAND VALIDATION
Cropland is defined as annual crops, i.e. sown or harvested in a year, excluding pastures and fallow, greenhouse Permanent crops are not included! Geometry: Proba-V 300m Minimum classification unit: subpixel ~60m x 60m

12. Spatial distribution of results: cropland in %
Majority rule applied at subpixel level; Classification unit: Proba-V 300m x 300m

13. Spatial agreement between classifications
0 – agree that is no cropland
0<…<1 – partial spatial agreement on cropland
1- fully agree on number of subpixels covered by cropland

14. Zoom-in in Africa Spatial agreement between classifications Cropland %
Note: In yellow – no cropland at both maps

15. SIGMA Campaign: CROPLAND VALIDATION
Duration: 3 weeks
Number of participants: 92
Number of validations: sample sites
Number of observations per site: 5
Check our video:
Join us on Facebook:
https: //

16. SIGMA Campaign: CROPLAND VALIDATION
Cropland is defined as annual crops, i.e. sown or harvested in a year, excluding pastures and fallow, greenhouse Permanent crops are not included! Geometry: Proba-V 300m Minimum classification unit: subpixel ~60m x 60m

17. Spatial distribution of results: cropland in %
Majority rule applied at subpixel level; Classification unit: Proba-V 300m x 300m

18. Spatial agreement between classifications

19. Zoom-in in Africa Cropland % Spatial agreement between classifications
Note: In yellow – no cropland at both maps

20. Thanks! Earth Observations Group
Steffen Fritz, Linda See, Ian McCallum, Christoph Perger, Dmitry Schepaschenko, Myroslava Lesiv, Inian Moorthy, Anatoly Shvidenko, Carl Salk, Martina Duerauer, Mathias Karner, Tobias Sturn, Christopher Dresel, Dahlia Domian, Antonia Dunwoody, Olha Danylo, Juan-Carlos Laso
Earth Observations Group
Ecosystems Service and Management (ESM)