Utilizing low-cost unmanned vehicle (UAV) to build orthophotos and Digital Surface Models

Utilizing low-cost
unmanned vehicle
(UAV) to build
orthophotos and
Digital Surface
Models
Britta Ricker, PhD
Assistant Professor
5/16/15 @bricker USGS Brownbag 1
http://faculty.washington.edu/bricker0/uav.html

About my research

Outline
1. Context - the problem/opportunity
 Unmanned Aerial Vehicles (UAV)
 Paradigm shift: digital globes! &
Citizen Science
2. How to generate your own
imagery
3. Additional considerations
4. Future Research

Landscape is
evolving
Constant need to
monitor this
change

Gaspar Felix
Tournachon
AKA Nadar,
Paris 1858
Kites
Balloons
Pigeons

Early aerial
photo of a
Boston
shoreline
1860
"Boston, as the Eagle and the Wild Goose See It"
by James Wallace Black

3rd Balloon Company
Fort Lewis 1938

Tacoma Narrows 1959

Photogrammetry is the use of photography in
surveying and mapping to measure distances
between objects.
The science applied, technique and
art of collecting reliable information
from stereoscopic models to
determine the shape, position and
size of objects in space.
The word Photogrammetry, is derived
from three words of Greek origin:
"Photon" - light;
"Graphos" – written or description;
"Metron" – to measure.

Remote Sensing Applications
Environmental monitoring
Natural Resource Management
Epidemiology
Geology – Hazard monitoring and mitigation
Land use – Agriculture, soil moisture
Search and Rescue
Animal tracking
(Colomina & Molina, 2014; Everaerts, 2008;
Forance et al., 2014; Upton et al, 2015)

Traditional Geographic Information Systems
and Science
Technical Training
Difficult to use
Expensive
Precise & Accurate

Geoweb and Digital Globes
• 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)

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)

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

Tell me, I forget.
Show me, I remember.
Involve me,
I understand.
-Chinese Proverb
(Paulos et al., 2009)

drones and enthusiasm
469,000 results on google

YouTube search “drone”=
3,440,000 (March 2015)
5,900,000 (Dec. 2015)

How drones are being used

How/Can enthusiasm be harnessed and incorporated
into (participatory) mapping and citizen science?

Drones!
Evoke negative connotation
(Sandvik & Lohne, 2014)
– Militarization
– Surveillance (Crampton et al.,
2013)

Drones for Good
“Humanitarian weapon” (Sandvik & Lohne, 2014)
“Predators for Peace” (Chow, 2012)
Humanitarian
Search and Rescue

Drones and Enthusiasm and Citizen
Science #dronesforgood
Meyer (2015) Digital
Humanitarianism
Ch. 4 “Crowd Computing
Satellite and Aerial
Imagery”
Knowledge sharing
communities
• Micro Mappers
• UAViators - Humanitarian
• DIYDrones
• OpenDrone
Map
• droneAdventures
• Drone Mapper
• Conservation Drones
• Drone code
• …

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

Research Aim:
Identify a feasible workflow to create maps from
aerial imagery captured from drones
for citizen science initiatives (= cheap + easy)
Img- pix4D

Partnering with local
scientists
to monitor gradual
changes
1. noxious algal blooms
2. eel grass beds
Monitor acute change
Landslide
Spring 2015
Des Moines WA

Key Considerations
1. Pre-Flight
2. Flight
3. Image Processing
4. Post-processing

Pre-flight: What type of UAV do I need?
UAV classifications
• Flight characteristics
• Takeoff and landing methods
• Source of Power
• Weight (including payload)

Drone classifications
Takeoff and landing methods
Fixed Wing (Airplane) Rotor Wing
(Helicopter)

Which Drone to buy?
Drone Specs
5/16/15
@bricker USGS Brownbag 30
Eisenbeiss, 2008 http://www.igp-data.ethz.ch/berichte/blaue_berichte_pdf/105.pdf

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

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

Pre Flight:
Read the manual!
Plan your flight.
(Autopilot versus free flight)
Have a back up plan.
(Nex & Remondino, 2013)

Flight
• Angle of Nadir

Try to Avoid
this…

Cost of the drone
is not the
only cost
Key Considerations
1. Pre-Flight
2. Flight
3. Image Processing
4. Post-processing

Processing Images
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…

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

Stitch imagery
• ArcGIS – Georeference
• QGIS
• MapKnitter

Needed Automation
Needed Measurements
Needed Photogrammetry!
Influenced metadata associated with the photos

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

Computer Vision
• field that includes methods for acquiring, processing,
analyzing, and understanding images and, in general,
high-dimensional data from the real world in an effort to
produce numerical or symbolic information, e.g., in the
forms of decisions.
• Applications:
– Recognition
– Motion Analysis
– Scene reconstruction
– Image restoration
– Artificial intelligence

Structure from Motion
“a range imaging technique; it refers to the process of
estimating three-dimensional structures from two-
dimensional image sequences which may be coupled with
local motion signals. It is studied in the fields of computer
vision and visual perception. In biological vision, SfM refers
to the phenomenon by which humans (and other living
creatures) can recover 3D structure from the projected 2D
(retinal) motion field of a moving object or scene.”
Wikipedia

Structure from Motion basics
1. Detection of feature points
in view
2. Tracking feature points
from one frame to the next
frame
3. Robust estimation of 3D
position of these points,
based on their motion
http://www.invision-news.de/artikel/104202.htm

Structure from Motion basics
Epipolar Geometry
perspective projection model
http://www.cs.columbia.edu/~jebara/htmlpapers/SFM/sfm.html

Free and Open Source Software (FOSS)
• Free as in speech
• Free as in beer

Created and Run by Steven Mather
https://github.com/OpenDroneMap/OpenDroneMap
http://opendronemap.github.io/odm/
Point Cloud
Digital Surface Model (DSM)
Textured DSM
Digital Elevation Models (DEM)
Orthophotography

AgiSoft

Pix4D

Pix4D Mobile App – for camera
Flight Path
Auto Pilot
Free flight
https://support.pix4d.com/hc/en-us/articles/202557269--Android-Pix4Dmapper-Capture-App-Getting-started
DJI offers three Software Development Kits (SDK)
Mobile: Aerial Imagery, Live Video, Navigation
Onboard: Flight data, flight control, data transmission
Guidance: Hardware, ports, velocity, filters, depth

Pix4D: Autopilot App

Federal Way Twin Lakes July 2015
Lake Loreene Lake Jean
Individual photos

Water is difficult to render
and sometimes ugly
Lake Jean
Lake Loreen

But Not Impossible
http://faculty.washingto
n.edu/bricker0/google_ti
les/Mission_00002_mosaic
.html

errors

Lake Wapato

Eel grass images

Acute Change
Landslide
Des Moines, WA

Landslide in Des Moines, WA
Pix4D Online processed 34 images in 4 minutes

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

Digital Surface Model
(DSM)

Pix4D

Digital Surface Model (DSM)

Interactive models
• https://sketchfab.com/models/62bf2c2b531e4e8680fed38
97b36b622
• https://sketchfab.com/models/ca1846e7863c405b9eee14
f63b278c61
• https://sketchfab.com/models/d561e5cb65b941c98566ae
18bb1bc1f0
• My website
http://faculty.washington.edu/bricker0/uav.html

Relative Accuracy
Absolute Accuracy
support.pix4d.com/hc/en-us/articles/202557489
http://faculty.washington.edu/bricker0/report_DJI/

Lessons Learned
• UAVs take practice to fly.
• Proprietary Software is expensive but easy to use!
• Open Source Software is an option if you have time and
technical knowhow.
• Water is difficult to georeferenced.
• Citizen science is an opportunity to continue to explore.
S 252ND ST
S 251ST ST
8THPLS
24953
25107
25129
25121
848
848
25118
25126
25121
25129
25107
24953
25110
S 251ST ST
8THPLS8THPLS

Additional Considerations
• Legal
• Ethical
– Surveillance
– Critical Cartography
• Data processing and sharing
• Practical
– weather
– other obstacles (trees, buildings,
birds, people)

Future Research Dream:
Develop an online platform for
1. UAV flyers and owners to share/donate photos
2. Provide a cloud based platform for image processing
3. Volunteers (citizen scientists) classify imagery
4. Expert Scientists verify results?
5. Share new knowledge!
Other project ideas:
3D print a model rendered with UAV
Algal Boom full season – 1 week
intervals
Multispectral sensor

Outline
Context - the problem/opportunity
 Unmanned Aerial Vehicles (UAV)
 Paradigm shift: digital globes! &
Citizen Science
How to generate your own imagery
Additional considerations
Future Research

Thank you!
Acknowledgements: Kris Seymer, Andrew James, Ali Modarres

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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