This session highlights how Earth observation data shared in the cloud is accelerating research in machine learning that can have a dramatic impact on the effectiveness of future warfighting capability. Come learn about SpaceNet, a project sponsored by CosmiQ Works, DigitalGlobe, and Nvidia that makes commercial satellite imagery available for machine learning research on AWS. In this session, you will learn how AWS machine learning services like SageMaker, hyper-scale GPU compute capacity, and datasets shared in the cloud can ultimately produce machine learning models that could have a dramatic impact on the effectiveness of future warfighting capability. As the DoD strives to bring innovation directly to the warfighter, a combination of global open data sets coupled with easily built ML models can give warfighters access to critical information when the mission needs it most.

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Jed Sundwall
Global Open Data Lead, Public Sector, Amazon Web Services
Balaji Iyer
Machine Learning Business Development Lead, Public Sector, Amazon Web Services
Machine Learning, Open Data, and
the Future WarFighter

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Open Data on AWS

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Why does AWS care about Open Data?
Many of our public sector
customers are required to make
their data available to the public.
Sharing data on AWS makes it accessible to a large and growing community of
researchers, entrepreneurs, and enterprises who use the AWS Cloud.
Many of our commercial sector
customers rely on access to open
data to develop their products.

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The AWS Open Data program
expands access to data by
staging it for analysis in the
cloud.
https://opendata.aws

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AWS Public Datasets
https://registry.opendata.aws

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“No matter who you are, most of
the smartest people work for
someone else.”
— Bill Joy, co-founder of Sun Microsystems

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Traditional Data Acquisition
“…data must be organized, well-documented, consistently formatted,
and error free. Cleaning the data is often the most taxing part of data
science, and is frequently 80% of the work.”
— Data Driven by DJ Patil and Hilary Mason

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Data Acquisition in the Cloud
Amazon S3
Amazon EC2
Amazon Athena Amazon EMR
Amazon Redshift
AWS Glue AWS Data Pipeline

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Undifferentiated Heavy Lifting

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Advantages of sharing data in the cloud
Global community of users
Reduced time to insight Lower cost of research
New services and tools

11. Landsat on AWS

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Staging data for analysis in the cloud
Amazon S3 allows programmatic
and precise access to data at
planetary scale.
Landsat on AWS uses Cloud
Optimized GeoTIFFs that allow
users to get only the data they
need when they need it.
cogeo.org
AWS Lambda.tarUSGS
Cloud Optimized GeoTIFFs
s3://landsat-pds

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Landsat-tiler from Mapbox
https://github.com/mapbox/landsat-tiler

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Graph by Drew Bollinger (@drewbo19) at Development Seed
Landsat on AWS

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Elevation
Models
Aerial
Imagery
Climate
Models
Satellite
Imagery
High-resolution
Radar
aws.amazon.com/earth

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Elevation
Models
Aerial
Imagery
Climate
Models
Satellite
Imagery
High-resolution
Radar
aws.amazon.com/earth/research-credits

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Machine Learning on AWS

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ML @ AWS
OUR MISSION
Put machine learning in the
hands of every developer and
data scientist

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FRAMEW ORKS / INTERFACES
KERAS
PLATFORMS
APPLICATION SERVICES
R E K O G N I T I O N R E K O G N I T I O N
V I D E O
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X
Amazon SageMaker Amazon Mechanical Turk
The AWS Machine Learning Stack

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BUI LD
Where should you spend your time?
Design training experiments

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BUI LD T RAI N
Where should you spend your time?
Run scalable training

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BUI LD T RAI N
Where should you spend your time?
Tune Hyperparameters
T UNE

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BUI LD T RAI N DEPLO Y
Where should you spend your time?
T UNE
Deploy and operate

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Data
Selection
Data
Cleansing
Data
Annotation
Model
Selection
Model
Evaluation
Model
Deployment
Data readiness challenges

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Pre-built Jupyter
notebooks
Built-in, high
performance
algorithms
T RAI N DEPLO Y
BUI LD
Build, train, tune, and host your own models
T UNE
Amazon SageMaker

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Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
BUI LD T RAI N & T UNE DEPLO Y
Build, train, tune, and host your own models
Hyperparameter
optimization
Amazon SageMaker

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Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
DEPLO Y
BUI LD
T RAI N &
T UNE
Build, train, tune, and host your own models
Amazon SageMaker

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Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
BUI LD T RAI N & T UNE DEPLO Y
Build, train, tune, and host your own models
Hyperparameter
optimization
End-to-end-encryption
End-to-end VPC support
Compliance and audit capabilities
Metadata and experiment management capabilities
Pay as you go
Amazon SageMaker

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Remove
undifferentiated
heavy lifting of ML
Iterate, iterate,
iterate!
Training speed Inference speed
Speed in all phases
Amazon SageMaker

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Collect and
prepare
training data
Choose and
optimize
your ML algorithm
Set up and manage
environments for
training
Train and tune
model
(iterate, iterate)
Scale and manage
the production
environment
Deploy model
in production
Amazon SageMaker

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Customer Success With Amazon SageMaker

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Machine Learning and Open Data on
AWS

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33
SpaceNet

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The SpaceNet Dataset is an open repository of over 5,700+ km2 of satellite
imagery across 5 cities, 520,000+ vectors, and a series of challenges to
accelerate geospatial machine learning.
Automated Mapping
Challenge: Building
Extraction Rounds 1 & 2
Nov. 2016 – Jun. 2017
High Revisit Challenge:
Off-Nadir Object Detection
Launching Spring 2018
Automated Mapping
Challenge:
Road Network Extraction
Nov 2017 – Feb 2018

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SpaceNet Challenge Winning Examples
AOI 2 Vegas: Image 1014 AOI 3 Paris: Image 1729 AOI 5 Khartoum: Image 991
Challenge prizes totaling > $100K to date
https://spacenetchallenge.github.io/

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Functional Map of the World Challenge

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Functional Map of the World Challenge
Managed by the Intelligence Advanced Research Projects Activity (IARPA), the
challenge published more than one million points of interest from over 200
countries. The dataset contains satellite-specific metadata that researchers can
exploit to build a competitive algorithm that classifies facility, building, and land use.
• Challenge wrapped up with ~60 contributors proposing trained models
• Winning solutions have been open-sourced with the goal of pushing the
community forward
• Data will be moving to SpaceNet as a permanent home on AWS

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DIUx xView 2018 Detection Challenge

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DIUx xView 2018 Detection Challenge
Defense Innovation Unit Experimental (DIUx) and the National Geospatial-
Intelligence Agency (NGA) are releasing a new satellite imagery dataset to advance
key frontiers in computer vision and develop new solutions for national security and
disaster response.
• Seeks to build off of SpaceNet and FMoW to apply computer vision to the
growing amount of available imagery from space so that we can understand the
visual world in new ways and address a range of important applications.
• Four main goal areas: Reduce minimum resolution for detection; Improve
learning efficiency; Enable discovery of more object classes; Improve detection
of fine-grained classes.
• Data is available on AWS and the challenge is currently under way with
$150,000 in prizes.

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