1. AYDES-CS (Crowdsourcing) (KİTLE KAYNAK TABANLI ANALİZ)
ENTERPRISE CROWDSOURCING PLATFORM
for GEOSPATIAL IMAGE ANALYSIS
Muhammed KALKAN
*: Crowdsourcing

2. Famous Example: Wikipedia.
What is Crowdsourcing?
Crowdsourcing is the term used to describe the process of getting work-force from a crowd of people in an online setting.
Famous Example: Wikipedia.
In 14 years, Wikipedia has become the first reference source with its many volunteer writers and a few specialist editors .
Wisdom of Crowd: The quality of crowdsourcing is described as large groups of people are smarter than a few number of elite, no matter how brilliant they are or better at solving problems.

3. Crowdsourcing for Geospatial Data
Using only image processing algorithms is not sufficient to produce generalised damage assessment results.
Even if disaster type is same, is it possible to find an image processing algorithm that works for all image types?
Rural -building types
Urban –
building types
Industrial -building types
As a result, Image Exploration and Damage Assesment Analysis needs «Human in the Loop» systems.

4. Crowdsourcing for Geospatial Data
In todays global world, a disaster is never related to a nation itself. It becomes an international problem and it must be handled in a collaborative manner.
Haiti Earthquake, 2010
Japan Tōhoku Earthquake and Tsunami, 2011
Philippines Haiyan Typhoon, 2013
Malaysia Airlines Disappearing Flight 370, 2014
These kind of disasters increased importance of
maps
geospatial data of the impacted areas
geographically-distributed information sharing communities.

5. HAITI EARTHQUAKE and CROWDSOURCING
BİLGİ
Satellite: QuickBird
Resolution: 0.64m
City: Port-Au Prince
File Size: 779MB
Region: 360km2
SORU
We need Rapid Damage Assessment Report After the Earthquake
Constraints
Time
Efficiency
Kitle Kaynak Tabanlı Görüntü Analizi Dr. Fatih KAHRAMAN

6. Kitle Kaynak Tabanlı Görüntü Analizi Dr. Fatih KAHRAMAN
CLOSER LOOK
Kitle Kaynak Tabanlı Görüntü Analizi Dr. Fatih KAHRAMAN

7. CROWDSOURCING for HAITI EARTHQUAKE

8. Task Division Strategies on Crowdsourcing Platform
Several different division strategies presented to
divide problem into small pieces and distribute these to the crowd
combine resolutions from the crowd
These strategies are
Parallel Iterative Division
Cascade Iterative Division
Serial (Non-Iterative) Division

9. Parallel Iterative Division
Each small piece in image analysed by at least two people
Plain image piece (wout any tags) presented to each user
Analysis results combined using statistical evaluation methods and reliability of the user
Volunteer-1
Statistical
Reliability Analysis
Volunteer-2
...
Reliability values assigned to each results
Volunteer-n
Dr. Fatih KAHRAMAN

10. Cascade Iterative Division
Similar to «Parallel Iterative Division»
Analysis done on small image piece before shown to each user
User taking labelling done by other user into account during analysis
Sequential analysis of image pieces
Analysis result determined by final user
Volunteer-1
Volunteer-2
Volunteer-3
Volunteer-n

11. Non-Iterative Division
Each small piece of image assigned to a different user
Analysis of each piece by only one user
Enables us to have analysis result of whole image in a fast manner

12. AFAD CROWDSOURCING PLATFORM (AYDES-CS PORTAL)

13. AYDES-CS END USER PERSONEL DASHBOARD (CLIENT)

14. AYDES CROWDSOURCING PLATFORM (CLIENT ANALYSIS INTERFACE)
Dr. Fatih KAHRAMAN

15. AYDES CROWDSOURCING PLATFORM (ADMIN INTERFACE)

16. AYDES CROWDSOURCING PLATFORM (ADMIN INTERFACE)

17. AYDES CROWDSOURCING PLATFORM (SOCIAL MEDIA INTERFACE)