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GEOSPATIAL EXPLOITATION PRODUCTS

Big Geospatial Data + Deep Learning + High Performance

Computing = Geospatial Intelligence

Bingcai Zhang

Tech Fellow

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Do We Have Enough Parking?

– Demo

– 300 drone images

– GSD = 3.5cm

– 1600 cars detected

– 99% detection accuracy

– 6 pixel positional accuracy

– 10 degree orientation accuracy

– 0.001% false positive error rate

– Data from Prof. Dunn

– Model trained with 7.5cm GSD

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What Is Deep Learning?

It works just like the brain (least favorite definition according to LeCun)

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car

0.5

not car

0.5

car

1.0

not car

0.0

IEEE SPECTRUM

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Simplicity Learning

– Object detection very complex

– Breakup objects

• Learn one type of object at a time

• Detect one type of object at a time

– Inspired by 4 year old pre-school best learning practice

• Learn one alphabet per week

• Learn letter “A” five days in a row (reinforcement learning)

– Inspired by drug discovery

• One specific drug for one specific disease

• No panacea

– Based on two decades of research experience

• Transform a complex problem into its simplest components

• Solve each component one at a time

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Data Normalization vs. Data Augmentation

– Scale normalization vs. scale augmentation

– Color normalization vs. color augmentation

– Rotation normalization (geospatial images)

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Simplicity Learning vs. Non-Simplicity Learning

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vehicle and anything else vehicle, stop sign, and

anything else

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Handcrafted Automatic Feature Extraction

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3D Features (3D Glasses)

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Rotation Variant Object Detection

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-88 -80 -72 -64 -56 -48 -40 -32 -24 -16 -8 0 8 16 24 32 40 48 56 64 72 80 88

avg

min

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std

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Rotation Variant Object Detection… 2

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0

0.2

0.4

0.6

0.8

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1.2

-88 -80 -72 -64 -56 -48 -40 -32 -24 -16 -8 0 8 16 24 32 40 48 56 64 72 80 88

probability

probability

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Rotation Variant Object Detection… 3

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Rotation Variant Object Detection… 4

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Singular Classification

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c

j

jz

iz

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esoft

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:max

Open-ended negative training examples problem

Not work just like the brain

Three new algorithms reducing false positive by 10

times

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Human vs. Machine

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– Human: Radoslav Gaidadjiev

• Two master’s degree

• Twenty years of experience with imagery

– Machine: DeepObject with one K40 GPU

– Human achieved accuracy of 99.9%

• Understand parking lot

– Machine achieved accuracy of 99.3%

– Demo

• Everyone participates human vs. machine

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Quality Training Samples = $

– Quality training samples are the new currency in deep learning

• Non-deep learning:

• AOD failed to recognize a car and an image analyst found this missing car

• A DR is generated and the cost to fix this DR is $1000

• Deep learning:

• This missing car could be automatically collected as a positive training example and

added to the training sample database

• Train deep learning network again with the new training example database

• Cost could be as low as $10

– Potential cost saving is very significant

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Mistakes = Quality Training Samples

– We learn from our mistakes (so does deep learning)

• Not all training samples are created equal

• Mistakes are more likely to have greater gradient

• Have stronger influence on decision boundary

• Quality of training samples is as important as quantity of training samples

• Data augmentation increases quantity

• Mistakes increases quality

– Future geospatial intelligence software should collect users intelligence

– Every mistake could translate into an enhancement to geospatial

intelligence software

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Future Intelligent Geospatial Intelligence System

– An intelligent system that could become smarter and smarter by learning

from its mistakes

– An intelligent system that could detect and monitor defense relevant

objects at 99% accuracy

– With 99% accuracy, this may be the game changer in geospatial

intelligence domain

– Significantly reduce software engineering and enhancement costs

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Questions?

Dr. Bingcai Zhang

858-592-5218

bingcai.zhang@BAESystems.com

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