CUGOS Spring Fling May 13, 2015 UW Seattle

1. Look what I can do!
Harnessing drone enthusiasm for
Citizen Science initiatives
Britta Ricker, PhD
University of Washington Tacoma
CUGOS Spring Fling 2015 UW Seattle
5/13/15 @bricker #CUGOS2015 #dronie 1

2. Look what I can do!
Harnessing drone enthusiasm for
Citizen Science initiatives
Britta Ricker, PhD
University of Washington Tacoma
CUGOS Spring Fling 2015 UW Seattle
5/13/15 @bricker #CUGOS2015 #dronie 2

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4. Background
• Geoweb:
Interconnected tools and data
available on the web that span
multiple geographic regions
and are geographically
associated (Lake & Farley, 2007; Haklay et
al., 2008)
• Revolutionized spatial data
(Sui, 2008)
• but not democratized (Haklay,
2013; Sui et al, 2013)
• What is significant = access
to satellite imagery!
(Goodchild, 2007; Harvey, 2014;
Kingsbury & Jones, 2009)
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5. Geoweb + Citizen Science
• Citizen Science
“scientific work
undertaken by members
of the general public,
often in collaboration
with or under the
direction of professional
scientists and scientific
institutions.” (Haklay,
2015)
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6. Tell me, I forget.
Show me, I remember.
Involve me, I understand.
-Chinese Proverb
(Paulos et al., 2009)
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7. Challenges in Citizen Science
• Getting and keeping people evolved,
excited and to keep coming back…
• Enthusiasm…emotional intensity
• Hardware accessibility and extensibility
• Software and data standards
• Privacy/Anonymity
• Authentication + Trust
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8. Why Drones for Mapping?
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9. Drones!
• Evoke negative
connotation
(Sandvik & Lohne,
2014)
– Militarization
– Surveillance
(Crampton et al.,
2013)
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10. Drones for Good
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“Humanitarian weapon” (Sandvik & Lohne, 2014)
“Predators for Peace” (Chow, 2012)
Humanitarian
Search and Rescue

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469,000 results on google
drones and enthusiasm

12. YouTube search
“drone”=
3,440,000
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13. How drones are being used elsewhere
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14. #Dronie Love
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15. Drones and Enthusiasm and
Citizen Science #dronesforgood
Meyer (2015) Digital
Humanitarianism
Ch. 4 “Crowd Computing
Satellite and Aerial
Imagery”
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Knowledge sharing
communities
• Micro Mappers
• UAViators - Humanitarian
• DIYDrones
• OpenDrone
Map
• droneAdventures
• Drone Mapper
• Conservation Drones
• Drone code
• …

16. Drones for Good
• Archaeology
• Emergency response
• Natural resource management
• Epidemiology
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• (Precision)Agriculture
• Mines and Quarries
• Construction
• Remote Sensing
(Colomina & Molina, 2014;
Everaerts, 2008; Forance et
al., 2014; Upton et al, 2015)

17. Potential benefits of using drones
for Citizen Science?
Temporal Resolution
Spatial Resolution
Increased coverage
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 Could complement or
augment existing data
collection methods (Crampton
et al., 2013)
 Big Raster Data
Drone image
Google Earth

18. How/Can enthusiasm for drone use be harnessed
and incorporated into participatory mapping for
citizen science initiatives?
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Research Question

19. Aim:
Identify a feasible workflow to create maps from
aerial imagery captured from drones
for citizen science initiatives (=cheap + easy)
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20. Key Considerations
1. Pre-Flight
2. Flight
3. Image Processing
4. Post-processing
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21. Partnering with local scientists
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23. Lake Wapato
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24. Drone classifications
• Flight characteristics
• Takeoff and landing methods
• Source of Power
• Weight (including payload)
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25. Drone classifications
Takeoff and landing methods
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Fixed Wing (Airplane) Rotor Wing (Helicopter)

26. Which Drone to buy?
Drone Specs
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Eisenbeiss, 2008 http://www.igp-data.ethz.ch/berichte/blaue_berichte_pdf/105.pdf

27. Cost of the drone
is not the
only cost
Key Considerations
1. Pre-Flight
2. Flight
3. Image Processing
4. Post-processing
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28. DJI Phantom Vision 2 +
• Flight time ~25 min
• Return to “home”
when battery is low
• Compass and GPS
• No fly zone
• Camera and gimble
• 14 megapixel
camera
• http://www.dji.com/product/phantom-2-vision-
plus/spec
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29. Pre Flight:
Read the manual!
Plan your flight.
Have a back up plan.
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(Nex & Remondino, 2013)

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MapBox
ESRI
Are you in a “No Fly Zone”?

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Try to Avoid
this…
@bricker #CUGOS2015 #dronie

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33. Flight
• Angle of Nadir
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34. Flight
• Angle of Nadir
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35. Manual Processing Strategies
1. Correct Fisheye
2. Process (Stitch) imagery – orthophoto
3. Post -Process imagery
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36. Processing Images
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Share Online
Software
selction
Stitch
Imagery
Capture
imagery
Drone Flying Fun!
Manually
PS/GIMP
ArcGIS/QGIS
Automatically
Pix4D
Agisoft
OpenDroneMap
Map Knitter, Google Earth, PDF, MapBox,
GeoServer + OpenLayers, ESRI Online…

37. Processing Images
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Share Online
Software
selction
Stitch
Imagery
Capture
imagery
Drone Flying Fun!
Manually
PS/GIMP
ArcGIS/QGIS
Automatically
Pix4D
Agisoft
OpenDroneMap
Map Knitter, Google Earth, PDF, MapBox,
GeoServer + OpenLayers, ESRI Online…

38. Manual
correction
and stitching
Correct Fisheye
using GIMP
Filters→Distorts→Lens distortion →
In the new window change Main to -
55 & Edge to 10 modify as needed
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39. Stitch imagery
• ArcGIS – Georeference
• QGIS
• MapKnitter
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40. Processing Images
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Share Online
Software
selction
Stitch
Imagery
Capture
imagery
Drone Flying Fun!
Manually
PS/GIMP
ArcGIS/QGIS
Automatically
Pix4D
Agisoft
OpenDroneMap
Map Knitter, Google Earth, PDF, MapBox,
GeoServer + OpenLayers, ESRI Online…

41. Software Options
Proprietary
• Pix4D
• Agisoft
• Other
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Open Source
• Visual Structure from
Motion System
(VisualSFM)
• Clustering Views for Multi-
view Stereo (CMVS)
• OpenDroneMap

42. Pix4D Mobile App – for camera
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Flight Path
Auto Pilot
Free flight
https://support.pix4d.com/hc/en-us/articles/202557269--Android-Pix4Dmapper-Capture-App-Getting-started

43. Landslide in
Des Moines,
WA
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44. Landslide in Des Moines, WA
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Pix4D Online processed in 4 minutes

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S 252ND ST
S 251ST ST
8THPLS
24953
25107
25129
25121
848
848
25118
25126
25121
25129
25107
24953
25110
S 251ST ST
8THPLS8THPLS
2012 2015

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S 252ND ST
S 251ST ST
8THPLS
24953
25107
25129
25121
848
848
25118
25126
25121
25129
25107
24953
25110
S 251ST ST
8THPLS8THPLS
2012 2015

47. Digital Surface Model(DSM)
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48. Pix4D
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49. Pix4D
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50. AgiSoft
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52. Open Drone Map
Created and Run by Steven Mather
https://github.com/OpenDroneMap/OpenDroneMap
http://opendronemap.github.io/odm/
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Point Cloud
Digital Surface Model (DSM)
Textured DSM
Digital Elevation Models (DEM)
Orthophotography

53. Major potential benefits
 Exciting and Fun
 Look what I can
do!
 Access to imagery
of high temporal
and spatial
resolution
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https://twitt
er.com/bric
ker/status/5
788965654
91802112

55. Lessons Learned
• Setting up base maps (raster) is challenging!
• Drones take practice to fly
• Proprietary Software is expensive but
increasingly easy to use!
• Open Source Software is coming along.
• Citizen science might not be ready…still need
specialists.
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56. 1. Drone owners share photos
2. Remote sensing (students) experts/ those
with programming skills (open drone map!),
access to expensive software (Pix4D) stitch
images
3. Volunteers (citizen scientists) classify
imagery
4. Expert Scientists verify results?
5. Share new knowledge!
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The Dream:
Develop an online platform for

57. Thank you!
CUGOS!, Andy James, Ali Modarres
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Masters of Science in Geospatial Technologies
http://www.tacoma.uw.edu/urban-studies/master-science-
geospatial-technologies

58. References
Balletti, C., Guerra, F., Tsioukas, V., & Vernier, P. (2014). Calibration of action cameras for photogrammetric purposes. Sensors (Basel, Switzerland), 14(9), 17471–90.
doi:10.3390/s140917471
Chow, J. (2012, April). Predators for Peace. Foreign Policy. Retrieved from http://foreignpolicy.com/2012/04/27/predators-for-peace/
Crampton, J. W., Graham, M., Poorthuis, A., Shelton, T., Stephens, M., Wilson, M. W., & Zook, M. (2013). Beyond the geotag: situating “big data” and leveraging the potential of
the geoweb. Cartography and Geographic Information Science, 40(2), 130–139. Retrieved from http://dx.doi.org/10.1080/15230406.2013.777137
Eisenbeiss, H., & Grün, A. (2009). UAV Photogrammetry. Institute of Photogrammetry and Remote Sensing (Vol. Doctor of). doi:doi:10.3929/ethz-a-005939264
Everaerts, J. (2008). The use of unmanned aerial vehicles (uavs) for remote sensing and mapping. The International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences, XXXVII, 1187–1192. Retrieved from http://www.isprs.org/proceedings/XXXVII/congress/1_pdf/203.pdf
Fornace, K. M., Drakeley, C. J., William, T., Espino, F., & Cox, J. (2014). Mapping infectious disease landscapes: unmanned aerial vehicles and epidemiology. Trends in
Parasitology, 30(11), 514–519. doi:10.1016/j.pt.2014.09.001
Haklay, M. (2013). Neogeography and the delusion of democratisation. Environment and Planning A, 45(1), 55–69. Retrieved from
http://www.envplan.com/abstract.cgi?id=a45184
Haklay, M. (2015). Citizen Science and Policy: A European Perspective.
Kingsbury, P., & Jones, J. (2009). Walter Benjamins Dionysian Adventures on Google Earth. Geoforum, 40(4), 502–513.
Lake, R., & Farley, J. (2007). Infrastructure for the Geospatial Web. In A. Scharl & K. Tochterman (Eds.), The geospatial web: How geobrowsers, social software, and Web 2.0
are shaping the network society (pp. 15–26). London: Springer.
Meier, P. (2015). Digital Humanitarians: How Big Data is changing the face of humanitarian response. Boca Raton, FL: CRC Press Taylor & Francis Group.
Nex, F., & Remondino, F. (2013). UAV for 3D mapping applications: a review. Applied Geomatics, 6(1), 1–15. doi:10.1007/s12518-013-0120-x
Paulos, E., Foth, M., Satchell, C., Kim, Y., Dourish, P., & Choi, J. (2008). Ubiquitous Sustainability: Citizen Science and Activism. In Workshop at the 10th International
Conference on Ubiquitous Computing (UbiComp 2008).
Sandvik, K. B., & Lohne, K. (2014). The Rise of the Humanitarian Drone: Giving Content to an Emerging Concept. Millennium - Journal of International Studies, 43(1), 145–164.
doi:10.1177/0305829814529470
Sui, D., Goodchild, M., & Elwood, S. (2013). Volunteered Geographic Information, and the Growing Digital Divide. In D. Sui., S. Elwood, & M. Goodchild (Eds.), (pp. 1–12).
Dordrecht: Springer.
Sui, D. Z. (2008). The wikification of GIS and its consequences: Or Angelina Jolie’s new tattoo and the future of GIS. Computers, Environment and Urban Systems, 32(1), 1–5.
Upton, V., Ryan, M., O’Donoghue, C., & Dhubhain, A. N. (2015). Combining conventional and volunteered geographic information to identify and model forest recreational
resources. Applied Geography, 60, 69–76. doi:10.1016/j.apgeog.2015.03.007
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