NON-EXPORT CONTROLLED THESE ITEM(S) / DATA HAVE BEEN REVIEWED IN ACCORDANCE WITH THE INTERNATIONAL TRAFFIC IN ARMS REGULATIONS (ITAR), 22 CFR PART 120.11, AND THE EXPORT ADMINISTRATION REGULATIONS (EAR), 15 CFR 734(3)(b)(3), AND MAY BE RELEASED WITHOUT EXPORT RESTRICTIONS. HARRIS.COM | #HARRISCORP Place image here (13.33” x 3.5”) UNCLASSIFIED DC8251 - SYNTHETIC DATA FOR TRAINING DEEP LEARNING REMOTE SENSING ALGORITHMS WILL RORRER, PRODUCT MANAGER Nvidia GPU Technology Conference – 22 – 24 Oct 2018
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NON-EXPORT CONTROLLEDTHESE ITEM(S) / DATA HAVE BEEN REVIEWED IN ACCORDANCE WITH THE
INTERNATIONAL TRAFFIC IN ARMS REGULATIONS (ITAR), 22 CFR PART
120.11, AND THE EXPORT ADMINISTRATION REGULATIONS (EAR), 15 CFR
734(3)(b)(3), AND MAY BE RELEASED WITHOUT EXPORT RESTRICTIONS.
HARRIS.COM | #HARRISCORP
Place image here
(13.33” x 3.5”)
UNCLASSIFIED
DC8251 - SYNTHETIC DATA FOR TRAINING DEEP LEARNING REMOTE SENSING ALGORITHMS
WILL RORRER, PRODUCT MANAGER
Nvidia GPU Technology Conference – 22 – 24 Oct 2018
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Agenda
• Background
• Humanitarian Aide and Disaster Relief (HADR) Needs
• Harris Deep Learning
• The Label Data Burden
• Synthetic Training Data
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“The GEOINT discipline has grown beyond the
limits of human interpretation and
explanation. The explosion of available data
diminishes the comparative advantage collection
provides. Instead, automated processing,
advancing tradecraft, human-machine
collaboration, and the ability to anticipate
behaviors will provide us a new advantage.”
Robert Cardillo,
Director of NGA
“We’re going to find ourselves in the not too
distant future swimming in sensors and drowning
in data”Lt. Gen. David A Deptula,
USAF Dep Chief of Staff for ISR 2010
"The skies will ‘darken’ with the hundreds of small
satellites to be launched by U.S. companies and
as procedures are developed to allow safe
operation of unmanned aerial vehicles in civil
airspace,"Robert Cardillo,
Director – NGA 2015
“So just how big is this rising tide? If we were to attempt to manually exploit the
commercial satellite imagery we expect to have over the next 20 years, we would
need eight million imagery analysts. Even now, every day in just one combat
theater with a single sensor, we collect the data equivalent of three NFL
seasons – every game. In high definition!
Imagine a coach trying to understand the strategy of his opponents by watching
every play made by every team in every game for three seasons – all in one single
day. Because three more seasons will be coming tomorrow. That’s what we ask
our analysts to do – when we don’t augment them with automation. But with all this
data – and dramatic improvements in computing power – we have a phenomenal
opportunity to do and achieve even more.”
Robert Cardillo,
Director – NGA 2017
A call to action: the urgency behind the adoption of AI for remote sensing
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14M training images
1,000 object categories
A call to action: the urgency behind the adoption of AI for remote sensing
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Establishment of the Joint AI Center – 27 June 2018
Key Points:
• Chartered by Deputy
Secretary of Defense
Patrick Shanahan
• “Overarching goal of
accelerating delivery of
AI-enabled capabilities,
scaling the Department-
wide impact of AI, and
synchronizing DoD AI
activities to expand Joint
Force advantages”
• Achieve goals by guiding
National Mission
Initiatives (NMIs)
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• Humanitarian Assistance and Disaster Relief Mission
• Potential benefits:
• Detect emerging disasters
• Improve response
• Quantify impact
• Save lives
• Possible application:
• Automated satellite & airborne imagery analysis
JAIC National Mission Initiative:Developing and Applying AI for HADR
Hurricane Wildfire
Flood Earthquake
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Natural Disaster Statistics
NOAA National Centers for Environmental Information (NCEI) U.S. Billion-Dollar
Weather and Climate Disasters (2018). https://www.ncdc.noaa.gov/billions/
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Answer: “Good” Labeled Data
To Define what a ‘Good’ label dataset is, first define how the desired algorithm is expected to be used
An example: the ubiquitous ‘Airplane Finder’
• If the algorithm is only expected to be applied to a very narrow distribution of images to make detections, a relatively narrow distribution of labeled training data is needed
• HOWEVER, if the algorithm is expected to be applied to a very wide distribution of images to make detections, a robust distribution of labeled training data is needed
A = brittle, B = brittle, C = robust = valuable
Algorithm robustness is largely driven by training label data robustness
Motivation: Algorithm Robustness What makes a “good” deep learning algorithm?
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Variations to Consider – Collection Geometry
Angles These identify the angle at which the sensor is imaging the ground, as well as the angular location of the sun with respect to the ground and image. These features can be added without preprocessing. The following angles are provided:
Off-nadir Angle Angle in degrees (0-90∘) between the point on the ground directly below the sensor and the center of the image swath.
Target Azimuth Angle in degrees (0-360∘) of clockwise rotation off north to the image swath’s major axis.
Sun Azimuth Angle in degrees (0-360∘) of clockwise rotation off north to the sun.
Sun Elevation Angle in degrees (0-90∘) of elevation, measured from the horizontal, to the sun.
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High Off Nadir Image Examples
Off nadir angle: 51°Off nadir angle: 32.7°Off nadir angle: 34.5°
Off nadir angle: 61°Off nadir angle: 61°
Increased deep learning algorithm robustness requires
exposure to a wide range of collection conditions
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Variations to Consider – Many Different Sensor Models
The democratization of space
• Many new sensors flying – offering much more persistent coverage
• However, this results in many different sensor models each with their own characteristics
• To make deep learning algorithms robust, they will need exposure to these varieties of sensor models
Increased deep learning algorithm
robustness requires exposure to or ability
to quickly adapt to multiple sensor models
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Algorithm RobustnessExample of real sensor and collection geometry variation