This presentation gives an overview of how data from drones is currently being captured, integrated for enterprise use, and the implications for the future of information technology (IT). You'll get details of the complex data types captured by drones. We showcase the value of using drones as an IoT device, create a vision for incorporating drone big data into existing enterprise business processes and software, and give seven best practice recommendations for IT departments

1. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
What Every CIO Needs to
Know About Drones, IoT, and
Big Data
Colin Snow
CEO and Founder, Skylogic Research

2. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
Big data: How big?
0
10
20
30
40
2009 2011 2015 2020
Growth of Data - Zettabytes
Source: Informatica
Image: MOTA
According to a 2015 CapGemini report,
60 percent of executives surveyed
believe big data will upend their
industries within 3 years.

3. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
IoT: How many?
Source: IoT Daily
Image: ramp
2020 – 14 Billion

4. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
Hobby
Open-source
Mass produced
Cheap
Commercial applications
Data capture device
Extensible
High ROI
Specialized use
Proprietary
Custom made
Expensive
Commercial drones as data capture
devices
Source: Skylogic Research

5. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
Big Data Drones
Focus has been on
analytics for
performance
improvements
Focus been on data
collection and
visualization
Focus has been on
devices and data
integration
IoT
Source: Skylogic Research

6. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
To some drones are a new kind of IoT
device because they:
 are not static
 are deployable
 can carry flexible payloads
 can capture multiple types of data
 can be re-programmable in mission
 can measure just about anything, anywhere
Source: Skylogic Research
“Putting wings on the internet of things”

7. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
Devices in motion: Challenges
 How to handle constrained resources?
 How to manage millions of things?
 How to communicate securely?
 How to deal with unreliable connections?
 How to handle geo-location?
 How to deal with map data?
Source: Amazon Web Services
Streams more than 1 TB of real-time map data per day

8. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
Characteristics of drone data
Non-standard IoT
 Geospatial
 Images, videos, binaries
 Various file formats, indexes, metadata
 Require image analysis to support
 Requires transformation and parsing

9. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
GIS file formats
 Raster
 Pixels
 TIFF, JPEG, etc.
 Static and RDBMS friendly
 Vector
 Points, lines, polygons
 Triangles represent the terrain surface
These are often combined, e.g., in a digital elevation
model (DEM) which is an image and vector data – 3D
Source: Wikipedia

10. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
Examples of processed data from
drones
 Orthomosaic
 Spectral
 Photogrammetry
 LiDAR
 Video
complexity

11. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
Unlike an uncorrected
aerial photograph, an
orthophotograph can be
used to measure true
distances, because it is an
accurate representation of
the Earth’s surface, having
been adjusted for
topographic relief, lens
distortion, and camera
tilt.
Orthomosaic - An orthophoto, orthophotograph or
orthoimage is an aerial photograph geometrically
corrected (“orthorectified”) such that the scale is
uniform: the photo has the same lack of distortion as a
map.
Source: Tim Boucher

12. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
Spectral Imaging – Example: Normalized Difference
Vegetation Index (NDVI) is an index describing
vegetation by showing the difference between near-
infrared (which is strongly reflected by vegetation) and
red light (which is absorbed by vegetation).
Source: Wikipedia

13. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
Photogrammetry is a technique which uses photography
to extract measurements of the environment. This is
achieved through the use of overlapping imagery;
where the same feature can be seen from two
perspectives it is possible to calculate measurements.
Source: Tim Boucher

14. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
LiDAR, which stands for Light Detection and Ranging, is
a remote sensing method that uses light in the form of
a pulsed laser to measure ranges (variable distances) to
the Earth. These light pulses—combined with other
data recorded by the airborne system— generate
precise, three-dimensional information about the shape
of objects and their surface characteristics.
Image: HeMav

15. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
The container format can
also contain
synchronization
information and metadata
such as GPS location and
directional data. This data
can also be encoded in
each frame. There can be
24, 30, 60, 120, and even
more frames captured per
second (FPS).
10 minutes of video at 30
fps = 18,000 frames
Video is almost always stored in compressed form to
reduce the file size for storage. A video file normally
consists of a container format containing video data in
a video coding format alongside audio data in an audio
coding format.
Image: Mistral Solutions

16. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
What can I see?
 How does it
change over time?
 How big is it?
 Where is it?
What is happening?
 Is anything moving?
 Where does it go?
 When does it get to
someplace
interesting?
Video data analytics
What is the value?
 Data to decisions
 Automation
 improves
exploitation of data
 Increases data
analysis efficiency
 Improves
performance
More GPU than CPU
Source: Kitware

17. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
Where’s drone analytics going?
ANALYTIC
TREND
NOW FUTURE
1. ANALYSIS Static analysis (single point in
time)
Trend analysis (changes /
exceptions over time)
2. APPLICATIONS Online reports and map apps Dedicated application specific
interactive mobile apps
3. STORAGE DBMS (disk storage resident)
and CPU
IMDB (in-memory resident) and
GPU
4. PROCESSING Post-processing On-board processing
Source: Skylogic Research

18. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
IT best practices for drone data
management
1. Data governance
2. Source aviation system access and APIs
3. Security and reliability along the “chain of custody”
4. Archive of source data for later re-processing
5. Master Data Management (MDM)
6. Privacy and risk mitigation
7. Access control
Source: Skylogic Research and SmartC2

19. November 17-18, 2015 ● San Jose Convention Center ● San Jose, CA
Connect:
• Web http://droneanalyst.com
• Twitter @droneanalyst
• Email colin@droneanalyst.com
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