For just a moment, think of the immense amount of data generated by Earth-observing systems. The sheer volume often makes it impractical for humans alone to perform the analysis, and accordingly, many groups are turning to artificial intelligence (AI) and machine learning (ML) algorithms to support their analysis. We'll hear from Development Seed and EOS about how they are using AI and ML to unlock the power of this planetary-scale data that is becoming increasingly more accessible in the cloud. From open-source libraries and human-in-the loop initial processing passes, to fully automated pipelines, we'll examine the new capacity for analysis now possible with technology.

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NaNa Yi
Engineer, Development Seed
Marc M Fagan
CEO, EOS Data Analytics
Machine Learning with Earth
Observation Imagery

2. Satellite Machine
Learning in the Cloud
Machine Learning with Earth Observation Imagery

3. Irrawaddy Delta, Burma(Myanmar)
Sentinel-2A, 07/2017

6. - ‘Fully automated’ machine
learning pipeline;
- Semi-automated machine
learning that will require our
professional mappers’ QA and
map objects.

7. Urchn
Tracking cities from space
‘Fully automated’ machine
learning pipelines backup
Urchn.
- Segmentation: for
detecting building
footprint and road
network.
- Object detection for
individual building
count.

8. http://cs224d.stanford.edu/index.html

9. Segmentation for detecting building
footprint and road network
Object detection for
building counts

10. Urchn
Tracking cities from space

11. Mapping high-voltage
grids in Pakistan,
Nigeria and Zambia
Semi-automated machine
learning that requires our
professional mappers’ QA
and mapping objects.
- Image classification
based supervised
machine learning
approaches.

12. Machine learning outputs Professional mapper tracing
The final output of high-voltage grids

13. - ‘Fully automated’ machine
learning pipeline;
- Semi-automated machine
learning that will require our
professional mappers’ QA and
mapping objects.

14. - ‘Fully automated’ machine
learning pipeline;
- Semi-automated machine
learning that will require our
professional mappers’ QA and
mapping objects.
How we did it

15. 1. Label Maker

16. 2. Spot Instance of AWS deep
learning AMI (don’t forgot to
turn on CloudWatch)

17. We open source our machine learning tools and
pipelines:
1. Satellite machine learning training data generation: Label Maker, Skynet-data;
2. Open source machine learning pipelines:
● Image classification:
High-voltage grids
AWS SageMaker case study
ResNet with AWS
● Image segmentation: Skynet
● Object detection: Tensorflow object detection
And we will open source have more tools and pipelines, stay tune!

18. Want to know more?
Email nana@developmentseed.org
Twitter @developmentseed
Website http://developmentseed.org

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153 Employees
Data Scientists, AI Engineers
Data (EO Sensor) Agnostic
Serving All non-profit and profit
Use Case Presentations - Bloomberg,
LP.

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“100% Convolutional Neural Network”
Disclaimer – lots of human “training or Indexing”
“100% Amazon”
Disclaimer – sometimes “fool around” with on-premise Gaming GPUs
All production - Storage, CPU, GPU, Products up on
AWS

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Challenge:
Agricultural crop production monitoring at high accuracy and cadence
is hard.
Present methods:
 Methods - Statistical extrapolation based on self-reporting
 Timelines - Months after harvest
 Cloud cover - Issue with regard to satellite analyses
 Costs – Satellite imagery (of sufficient resolution and cadence) –
expensive
InSAR Weekly Ag Crop Production - United
States

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Solution:
InSAR (Synthetic Aperture Radar) utilizing AWS Sentinel-1 (ESA) SAR
 Product – Measurement of every crop zone, crop type, by climate,
by season
 Timelines – Every 7 days (nationwide)
 Cloud cover – SAR negates cloud issues
 Costs – ESA Sentinel 1a & !b – open source
Novel, new and very valuable solution to the marketplace
InSAR Weekly Ag Crop Production - United
States

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• Bloomberg – first –of-its-kind financial industries data for
commodities, futures, derivatives, swaps trading
InSAR Weekly Ag Crop Production - United
States
• Producers– seed ,
manufactures,
logistics, storage,
food processors, etc.
• Governments and
non-profit

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Challenge:
Monitoring of millions of wildcat oil and gas upstream production
activities by lifecycle states as the earliest signals of actual oil
productive rates.
Present methods:
 Methods – human data gathering from public permit recorders
and statistical, periodic field statistical inspections
 Costs – expensive, low margin and value, by virtue of low
accuracy
Upstream Oil & Gas Production Monitoring

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Solution:
Convolutional Neural Net analyses of wellhead equipment,
configuration of wellheads indicating the lifecycle patterns indicating
type of production 2 months prior to inspection
 Product – instantaneous schedule of all wellheads within analyses
schedule
 Timelines – 1 per month ( possible sooner}
 Cloud cover – an open issue
 Costs – Fraction of costs due to revenue share and scale with
Partner (Airbus)
Upstream Oil & Gas Production Monitoring

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• Bloomberg – first –of-its-kind financial industries data for
commodities, futures, derivatives, swaps trading
Upstream Oil & Gas Production Monitoring
Product
• Producers – field
operations,
downstream
operators, equipment
suppliers
• Governments and
non-profit entities

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AWS Platform
EOS performs 100% of the use case operations on AWS including:
 Processing and storage of the entire United States (and UA and
UK)
 Earth on AWS Airbus Pléiades and SPOT access storage
and processing
 GPU and CPU processing of the Neural Net analyses
 Production environment of the final product
 Additional ancillary CCN object detecting e.g., preferable
road and tailing pond construction, etc.
Upstream Oil & Gas Production Monitoring

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Challenge
Technical methods to mask false night sky images anemology's e.g.
ice, cloud cover, water glare reflectance, etc. in sensor data at large
scale and cadence to create accurate night lights signals especial
with change
Present methods:
 Methods - small partial world area analyses
 Cloud cover – omitting regular (monthly) comprehensive regular
results coinciding with IMF published corollary results
 Inability therefore to back test results and modify CCH algorithms
 Costs – Utilization of open source NOAA NPP data
World Night Lights – Economic Analyses

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Solution:
Full monthly access to NOAA NPP data
 Product – Worldwide modeling of all countries highlighting relative
light intensities with change detection indices correlated to the
IMF rating Timelines – Monthly
 Cloud cover – None (masked)
 Costs – NOAA NPP imagery open source
World Night Lights – Economic Analyses

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• Bloomberg – first-of-its-kind financial industries data for Macro-
trend correlation to bi-annual GDP reporting way before IMF
release as well as methods to discount inaccurate reporting by
certain environmental entities
World Night Lights – Economic Analyses
 Multiple additional
uses e.g. energy
markets, population
trends, etc.
 Governments and
non-profit uses

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AWS Platform
EOS performs 100% of the use case operations on AWS including:
 Processing and storage of the entire world for NOAA NPP
 Largest data set to-date
 Completed making methodologies work within AWS with
EOS proprietary methods
 GPU and CPU processing of the Neural Net analyses
 Production environment of the final product
World Night Lights – Economic Analyses

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Speaker Contact
CEO
EOS Data Analytics
Menlo Park, CA
978 500 0344
Marc.Fagan@eosda.com
EOS.com
Marc Fagan

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Thank you!