Leveraging Earth Observations and Cloud Technology for Global Sustainable Development

P U B L I C S E C T O R
S U M M I T
Washington DC

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R
S U M M I T
Leveraging Earth Observations and Cloud
Technology for Global Sustainable
Development
Ana Pinheiro Privette
Amazon Sustainability Data Initiative Lead
Amazon
S e s s i o n 2 9 4 8 8 0

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Agenda
1. Helping Countries Realize the Potential of Earth Observations for
Sustainable Development
Steven Ramage (Group on Earth Observations)
2. Digital Earth Africa
Aditya Agrawal (D4D Insights)
3. Machine Learning for Earth Observations to support Global Development
Anne Hale Miglarese (Radiant Earth Foundation)
4. Panel Discussion and Q&A

S U M M I T
Amazon Sustainability Data Initiative:
promoting innovation for sustainability
Ana Pinheiro Privette
ASDI Lead
Amazon
S e s s i o n 2 9 4 8 8 0 : L e v e r a g i n g E a r t h O b s e r v a t i o n s a n d
C l o u d T e c h n o l o g y f o r G l o b a l S u s t a i n a b l e D e v e l o p m e n t

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Global Sustainable Development

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“…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
Removing Undifferentiated Heavy Lifting

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The Amazon Sustainability Data Initiative significantly
reduces the cost, time, and technical barriers associated with
analyzing large datasets to generate sustainability insights.

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ASDI
Knowledge
technical

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• Weather observations
• Weather forecast data
• Climate projections data
• Satellite imagery
• Hydrological data
• Air quality data
• Ocean forecast data
• Disaster

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Dashboards Machine Learning
Data
Warehousing
Big Data
Processing
Interactive
Query
Operational
Analytics
Real time
Analytics
Serverless
Data processing
Visualization & Machine Learning
Analytics
Amazon S3

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tutorials
blogposts
open code
informational sessionsin-person events
webinars

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Contact us: sustainability-data-initiative@amazon.com
Lear more: https://www.aboutamazon.com/sustainabilityamazon-sustainability-data-initiative

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2 9 4 8 8 0
Helping countries realize the potential of Earth
observations for sustainable development.

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GEO-AWS EARTH OBSERVATION
CLOUD CREDITS PROGRAMME
GEO is lowering barriers to entry for
developing countries to use cloud
services in partnership with AWS.

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The programme offers GEO members and
research organizations from developing
countries access to cloud services to help
with the hosting, processing and analysis of
big data about the Earth to inform
decisions for sustainable development.

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Earth observations are
data and information
collected about our
planet, whether
atmospheric, oceanic or
terrestrial. This includes
space-based or remotely-
sensed data, as well as
ground-based or in situ
data.

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GEO has members in 105 countries,
and the GEO-AWS collaboration has
broadened engagement to other
countries.

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GEO is an
intergovernmental
partnership that
improves the
availability, access,
understanding and
use of Earth
observations for the
benefit of society.

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Who can apply?
Agencies and research organizations
from GEO members categorized as
developing countries by UNDP.

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What kinds of projects
qualify?
Non-commercial projects that support
environmental and development goals,
including the Sendai Framework for
Disaster Risk Reduction, the Paris
Agreement and the United Nations
Sustainable Development Goals.

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Ongoing work:
The Africa Regional Data Cube provides users in five
countries with an analysis platform for satellite imagery
to address key development challenges, including food
security, disaster risk management, coastal erosion and
urban expansion.
Through work with NASA and GPSDD, AWS cloud
credits have been provided to the countries involved
for three years to reduce the data storage, processing
and analysis burden.

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What support will be
provided?
Recipients of cloud credits will also
receive support from the GEO
community and AWS experts to refine
and implement their projects for the best
possible results.

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GEO is supporting Open Data Cube
initiatives around the world on AWS.
https://www.opendatacube.org

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AWS is also collaborating with other
GEO activities internationally,
including
Digital Earth Africa.
http://www.ga.gov.au/digitalearthafr
ica

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www.earthobservations.org/aws.php

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Thank you!
S U M M I T
Steven Ramage
sramage@geosec.org

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Aditya Agrawal
Founder
D4DInsights
2 9 4 8 8 0

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Open Data Cubes
Enabling Decision Ready Products

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Making Satellite Data Free and Open

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Addressing the Demand
Countries have expressed a need for better access and capacity for applying Earth observation
data to national priorities, in relation to national development objectives, 2030 Agenda and Agenda
2063.
Digital Earth Africa will provide an operational data infrastructure deployable in the cloud or locally
that gives the government control over its management. The project will support a multi-stakeholder
and data ecosystem approach.

S U M M I T
Data Roadmaps for
Sustainable
Development
Support countries at national and sub-national
levels to develop and implement whole of
government and multi-stakeholder data
roadmaps for harnessing the data revolution
for sustainable development.

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Africa Regional Data Cube
A data cube provides analytically
ready data across decades allowing
for easily accessible geospatial
analysis on key environmental issues.

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Digital Earth Africa
Phase I Objectives
• Developing a coalition of partners and investors;
• Undertaking public communications including significant
stakeholder engagement and workshops
• Identifying the key political, institutional, financial, capacity
building and technical issues and opportunities
• Understanding the landscape to identify alignment
opportunities
• Build awareness, buy-in and approach for Phase II
• Identify potential funding sources for next phase

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Tracking agricultural change

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Local Scale
Water Quality Monitoring: Lake Burley Griffin

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There is a 13% loss in dense vegetation from 2000 to 2017. These
illegal mines have a significant impact to land and water resources.
2000 2017
Vegetation
Mask
Illegal Mining: Ankobra River, Ghana

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Drought resilience workflow - Tanzania
1. Set up analysis2. Import data, mask clouds3. WOfS Analysis4. Vectorize water bodies5. Compute % full for each date6. Show time series for each water body

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Drought status for Tanzania
Inundation percentage compared to
maximum waterbody extent

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Inundation % contours Interpolated ‘relative’ topography
Mapping relative waterbody topography

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Operational Data Cube for
All of Africa
• Operational Data Cube for whole of Africa
• Regular decision ready product / not research
• Automated, run for every pixel for entire
continent
• Levering off other ODC developments
• New institutional home- (Host TBD)~30 staff
• Flexible cloud/HPC Infrastructure
• Funded for production of product and capacity
building/App development
• Multilateral effort, not owned by one country
• Interoperability allowing for connections with other
platforms to share data and algorithms

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DE Africa Phase II –
Setting the Foundation
• Governance framework
• Country engagement
• User needs and requirements
• Technical roadmap and development
• Cloud infrastructure arrangements
• Partnership and alignment
• Capacity building plan
• Political engagement and launch events
• Recruitment and facilities

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Shout out to AWS
• Provided a grant in support of the
ARDC launch
• DE Africa will be based on cloud
infrastructure
• USGS L2 Landsat Data will be
available on AWS
• Awaiting data center in South Africa

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Digital Earth Africa 3 Year Strategy

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Thank you!
S U M M I T
Aditya Agrawal
aditya@d4dinsights.com

S U M M I T
2 9 4 8 8 0
Machine Learning for Earth Observations to
support Global Development
Open Data
Innovative
Technologies
Positive Impact

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CHALLENGE OPPORTUNITY SOLUTIONS
Dramatic
increase in
imagery supply
Rapid
Innovation
New
Solutions
Satellite
Drone
Aircraft
Cloud Computing
Machine Learning
Digital Twin
>
600
IOT
Block Chain

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What Machine Learning
Promises
Impact on International
Development
 Quickly recognize patterns and
generate insights from huge
geospatial datasets
 Improve and automate predictive
forecasting – e.g., crop yields,
disease outbreak, habitat
monitoring, illegal logging
 Optimize processes, predictions,
forecasting, etc.
Changing analytical models
Ideal Training Datasets Need
 Large and diverse across time and
space
Machine Learning
Machine
Derived Model
Predictions
Measured
Outcomes
Learning
Algorithms
Data
New
Data
Domain
Knowledge
Classical Data Analysis
Handcrafted
Model
Predictions
New
Data
Statistical
Theory
Data
Domain
Knowledge

S U M M I T
Machine Learning Challenges
Training Data Catalogs in Global South
 Lack of Geo-Diversity
 Scarce data sources
 Data Accessibility
 Inter-Operability
 Machine learning-readiness
Result of Gaps in Training Data Catalogs
 Biased or incorrect results
 Inability to capture wide range of
possible outcomes in space and time
Diversity of cropping patterns at a
global scale.

S U M M I T
Machine Learning Opportunities
Generate
thematic training
datasets
Create
transparency
through
collaborative
efforts
Aggregate
ground truth
data
Transfer
learning
methods
Innovate for
Sustainable
Development

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ML Commons for Earth Observation
Establish community focused on advancing the application of EO
data to solve challenges in the Global South using ML techniques
Open Source ML “Hub” for Earth
observation
Training Data,
Open Models &
Standards
Crowdsourced
Ground-Truth
Image Labels
Educate & Inform
Technical
Groups,
Fellowships,
Convenings
Market
Analytics,
Trends and
Thought
Leadership
Goal
Services

S U M M I T
• Built on ESA Sentinel-2
• 10 m resolution
• Crowdsourcing and citizen science to
validate labels
• Hosted on AWS
• Available via API to public with Creative
Commons license
Current ML Hub Training Datasets
Sponsored by:
Major
African
Crop Type
Global
Land
Cover
TrainingDatasetsin
theWorks
Sample training
data for crop type

S U M M I T
Get in touch Follow Us
hello@radiant.earth
www.radiant.earth
www.mlhub.earth
@OurRadiantEarth
https://www.facebook.com/OurRadiantEarth

S U M M I T
Thank you!
S U M M I T
Anne Hale Miglarese
anne@radiant.earth

S U M M I T
S U M M I T

Leveraging Earth Observations and Cloud Technology for Global Sustainable Development

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