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The power of the Cloud & the power of Partnerships Using satellite imagery in the SDGs: the case of 6.6.1 UN-GGIM Side Event 27 November 2017 Rebecca Moore Director, Google Earth, Earth Engine & Earth Outreach

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Page 1: The power of the Cloud & the power of Partnerships Using satellite imagery in …ggim.un.org/meetings/2017-Mexico/documents/Google_Earth... · 2017-12-18 · The power of the Cloud

The power of the Cloud & the power of Partnerships

Using satellite imagery in the SDGs: the case of 6.6.1

UN-GGIM Side Event

27 November 2017

Rebecca Moore

Director, Google Earth, Earth Engine & Earth Outreach

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Operating NASA Earth Science Missions

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Earth Observation Data Archives

6 Million Landsat images (1972-2017)

2 Petabytes stored on tapes at USGS

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-Jim Gray (1944-2007)

“Often it turns out to be more efficient

to move the questions than to move

the data.”

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Google Cloud: Data Storage co-located with

Processing

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earthengine.google.com/timelapse

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32 yearsof satellite data

5,000,000

+Landsat scenes analyzed

3quadrillion pixels

2MMore than hours of computation over 66,000 computers

~1.

5Elapsed time: days to build the mosaics

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The Earth Engine Public Data Catalog

> 200 public datasets > 6000 new images every day

> 11 million images > 8 petabytes of data

Our (long-term) mission: Organize all the world's Earth science data

and make it universally accessible and useful, with computation on top.

Today:

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Parallel-processing on thousands of CPUs

Massive scale of

Cloud-computation

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Original Image

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Original Imageis divided into 256px sub-units.

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Sub-units are distributed

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Sub-units are distributedto separate machines where they can be processed in parallel.

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Thousands can be processed

simultaneously.

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Result is reassembled

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Result is reassembledinto a finished image.

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THE BEGINNINGMAIN

Global Forests:Map, Measure, Monitor

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• Earth Engine (time series)

• StreetSmart

Imagery + Compute

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• Earth Engine (time series)

• StreetSmart

Imagery + Compute

654,178Landsat

Scenes

1,000,000Hours of

Computation

700Terapixels

of Data

10,000CPUs

Used

4Days to

Complete

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After citing Global Forest Watch on the floor, the

Philippines House of Representatives approved a new

bill prohibiting the destruction of mangrove forests.

Illegal deforestation in Peru was revealed using GFW’s

monitoring systems.

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Photo: Dibyendu Ash, www.goingwild.com

"This study was made possible by free availability of satellite

imagery, cloud computing services, and interactive web tools.

We were able to analyze 14 years of high-resolution

global forest loss data across 76 landscapes that span 13

countries."

http://advances.sciencemag.org/content/2/4/e1501675.full

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Confidential + Proprietary

Chasing deforestation through the clouds

Lineal deforestation feature growing deep into a

National Forest, identified by ISA in February

using their SIRAD app on ESA Sentinel-1 radar

data in Earth Engine.

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Image credit: New York

Times

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Global-surface-water.appspot.com

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Global-surface-water.appspot.com

3,066,102Landsat

Scenes

10,000,000Hours of

Computation

1,823Terapixels

of Data

10,000CPUs

Used

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Charlemagne at dinner; detail of a miniature from BL Royal MS 15 E vi, f. 155r (the "Talbot Shrewsbury Book"). Held and digitised by the British Library.

Processing 3 million scenes on one

computer would have taken 1,212 yearsProcessing 3 million scenes in

Google’s Earth Engine took 45 days

Start your computer running just after Charlemagne conquers Saxony in 804... leave it running 24 hours a day, 7 days a week and the water maps just might be ready today

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https://www.servirglobal.net/Global/Articles/Article/2527/bringing-the-power-of-google-to-scientists-in-the-lower-mekong

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680Buildings in

Maskelyne

Community

150Currently

Inundated

by High Tide

475Inundated at

High Tide by

2100

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DiSARM: Maps, Machine

Learning & Malaria

Problem: How to prioritize villages for

intervention and resource deployment, e.g.

bed nets?

UCSF and other orgs are using Earth

Engine to generate automated high-

resolution malaria risk maps in Zimbabwe

and Swaziland.

DiSARM’s mobile app can be used by the

malaria programs and field teams to target

interventions. The goal is to make their

monitoring more efficient and effective, and

to eliminate malaria in Swaziland in the

next 3 years.

Current maps used by the National

Malaria Control Program in

Swaziland.

Monthly risk maps generated by the

DiSARM platform, powered by Earth

Engine. UCSF Global Health Group

Learn more at disarm.io

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Rebecca Moore, Google

earth.google.com

earthengine.google.com

earth.google.com/outreach

Thank you!