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Global -scale analysis of Earth observation data

Google Earth Engine is a platform that utilizes Google's strengths in information and technology to address global challenges such as climate change, pandemic disease, poverty, and data management. It provides access to vast amounts of geospatial data and tools for processing and analyzing it at scale. The platform aims to make complex analyses simple and promote transparency, reproducibility, and reuse of scientific data and algorithms.

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Global -scale analysis of Earth observation data

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  1. The Google Earth Engine Global-scale analysis of Earth observation data Dave Thau thau@google.com

  2. Who are we?                         - Technology-Driven Philanthropy. Goal: Use Google's strengths in information and technology to address global challenges.

  3. Google’s Strengths Global Challenges Climate change Pandemic disease Poverty Data management, processing, Storage, indexing & delivery. Making things simple, at scale. RE<C and RechargeIT Developing utility scalerenewable energy cheaper than coal (RE<C) and accelerating the commercialization of plug-in vehicles (RechargeIT)‏ Google Flu Trends Google Flu Trends uses aggregated Google search data to estimate flu activity in near real-time in 20 countries. Google Power Meter Google PowerMeter is a home energy monitoring tool that gives you the information you need to use less electricity and save money. 3

  4. Motivation Rondonia, Brazil June 1975 Rondonia, Brazil July 1989 Rondonia, Brazil September 2001 UNEP: "Atlas of our Changing Environment"

  5. Challenges and Goals Access to Data • Cost, availability & licensing of petabytes • Raw data requires preprocessing Why should each user have to solve this on their own? Access to Software, Algorithms, and Hardware • What algorithms are available and where to find them? • I don’t have a cluster, what do I do? Non-technical Challenges • Open, transparent, verifiable • Privacy and sovereignty concerns Non-Goals • Doing global monitoring ourselves • Scientific algorithm research

  6. The Plan Developing a platform for processing at scale • Inherently parallel system • Instant visualization • Promote transparency, reproducibility, reuse Getting expertise from the experts • Avoid reinventing the (scientific) wheel • Partner with academic institutions, NGOs, indigenous people… Providing a place to publish (or not) • Let users curate their own datasets and algorithms • Help others to find them • Or keep everything private 6

  7. Demo

  8. Details Very fast computation of scientific map products • Using arbitrary (user-supplied) algorithms With some nice features • APIs for algorithm development and Web front ends • Access control, versioning, provenance • Online and Desktop versions (open source desktop version) • TIFF / KML / Fusion Tables / Google Maps • On a lot of data • Stored in their native projection • Every available Landsat scene • Every MODIS scene • Commercial datasets with cost pass-thru • User supplied data

  9. Android phones+OpenDataKit Training: Tanzania Village Forest Monitors, TZ Forestry and Beekeeping Division, Jane Goodall Institute, Nov 2009

  10. Each point: GPS-tagged photo, tree info, plot info, more….

  11. …more data collected for each in-situ sample point

  12. Dataset from Brian Heidorn Image from Animal Biodiversity Web

  13. Biodiversity and Conservation Prediction for Triatoma barberi Peterson, et al. 2002 Niche Modeling River health Your Ideas Upper Mississippi From USGS

  14. Thank you To sign up for our trusted tester list: earthengine-beta@google.com

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