Analyzing Terapixels and Megamiles with Google's Global-scale Geospatial Cloud Computing Platform

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In our relentless pursuit to map the world, Google has spent years building the world’s largest GIS. We process petabytes of satellite and aerial imagery, render millions of map tiles on the fly daily for serving to maps.google.com and through our Maps API, and process and publish millions of user-submitted corrections to our basemap.

Beyond the services that create our maps, we’ve also purpose-built the world’s largest geospatial global decision engine, capable of on-demand multi-spectral imagery and vector analysis — with a goal of permitting our users to make informed decisions from Google’s mapping data.

We live in an age of massive georeferenced datasets, prodigious spatially (e.g., global), temporally (e.g., spanning decades), and physically (e.g., consuming petabytes of storage). And yet despite this abundance of data, the ability to store, manage, and process it as Google does has typically been the domain of large institutions, requiring specialist training and a frequently massive investment in storage and computing power.

This talk will focus on several key technologies powering Google’s geospatial cloud computing platform, on how we’ve begun exposing these geospatial services for anyone to use, and will showcase several examples of customers using the services to help them solve their real-world problems.

Photo of Louis Perrochon

Louis Perrochon

Google

Louis Perrochon is an Engineering Director at Google and he currently leads the team that builds Google’s geospatial products for businesses, governments and non-profits. These products enable you to put “your data on the Google map” and include Google Earth, Google Maps Engine and Google Earth Engine. His team also processes all aerial and satellite imagery for Google Earth and Google Maps. Previously, he has been leading Engineering for YouTube, which processes over 72 hours of video per minute and serves over 4 billion videos a day. His academic credentials include a PhD in Computer Science from the Swiss Federal Institute of Technology and 3 years post-doctoral research at Stanford University.

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