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JOEL CONKLING: My name is Joel Conkling.
I'm a product manager for Earth Engine.
Earth Engine's the petabyte-scale processing
platform for geospatial data at Google,
used by a wide range of industries
we've been developing over the last 10 years.
And I'm excited to tell you a little bit about it
and show you a few demos that will illustrate
some of what it can do.
So when we think about Earth Engine,
we think about two things--
one is it's a massive data catalog
with open data from satellite imagery, and many others,
and their computation platform where you can analyze
that data in a variety of different ways
to meet your use cases.
We see users from agriculture industry, forestry industry,
oil and gas, natural resources, and a wide variety of others.
And we'll go through a use case here in a second.
And we'll do that through the lens of an agricultural use
case.
So imagine you're working at a company.
You have a customer who is a farmer in Arizona.
He wants to know how his agricultural fields are doing.
Maybe he's farming corn, wheat, soy--
something like that.
And we're going to go through a use case that shows
how that works in Earth Engine.
I mentioned the Earth Engine data catalog.
It's about 500 data sets of geospatial data, 30 petabytes
and growing with a wide range of data types,
from climate and weather data, imagery, and radar data,
geophysical, socioeconomic, air quality data, and many more.
You can bring your own data to our attention, as well.
So if you're doing analysis where
you like open data and your own data, that's easy to do.
And what that does is it lets you get started faster.
So instead of figuring out what data you need,
where do you find it, how do you download it,
how do you pre-process it--
what you can do with Earth Engine
is just identify that data in the catalog
and start your work-- start your hypothesis.
And so I'm going to go to a demo right now, which
will show us a little bit about how that works.
So here with five lines of code there
in the middle of the screen, we've
pulled some data in for analysis.
The first line identifies the data set.
The second line says, what date do we want to look at?
Here, on the third line, we're doing some visualization
parameters, adding it to the map,
and telling it what part of the map to show.
I hit Run.
And you can see this data set.
This is Landsat 8.
It's a satellite imagery program run by the US government.
It covers the globe every 16 days.
We've asked it for one day.
And you can see the path of the satellite
over the course of that day.
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JOEL CONKLING: So we haven't asked much of Earth Engine yet
in terms of computation.
But when you do start to do computation,
you often start in the Code Editor--
what we call the Code Editor.
And here at the top in the middle
is where you write your script.
Right above that, there's a Run button, which I used just now,
which gives you the ability to run calculations
in Earth Engine.
And then you can look at your results on top of the map,
either here at the bottom.
Or if you click somewhere on the map,
you can see the results in the top right.
You also have access to all of your scripts, all of your data,
and documentation about how to use Earth Engine
in the top left panel there.
In addition to the Code Editor environment,
we also have programming APIs.
You can use those programming APIs
in Jupiter colab notebooks--
environments like that-- or pull data into your GCP work flows
with APIs, as well.
I want to go through a series of additional demos
here where we'll start to answer that question for the farmer,
how are my fields doing this year?
The first thing I'll do is show you
what it looks like when you pull in 16 days of data, which
should give us pretty full coverage of the United
States and the whole globe.
So that's given the Landsat satellite
times to cover the entire globe.
As someone who's working to answer questions for a farmer,
you might care about a longer time series than that.
So I'll make this a six-month time series.
And you can hit Run there.
And so, again, with relatively few lines of code,
not that much work to do.
You're pulling in data from six months worth of Landsat.
Hundreds of gigabytes of data is at your fingertips.
And the Landsat catalog itself goes back 30 or 40 years.
So you can go way farther back in history of the map.
So we've also got this median value here,
which is basically looking at, over six months,
you've got 12 images for each part of the planet.
It's looking at the median value for each
of the bands in that image.
It turns out that clouds are highly reflective.
So, if you want to find clouds, change median to max.
And once that comes up, you'll see
that there are clouds almost everywhere over the United
States at some point during that six-month period.
A quick and easy way to mask out clouds,
which is a more common thing to do
is you change that median to a min and run that.
And you've got a decent cloud-free mosaic of the United
States or anywhere in the globe.
Again, pretty simple and easy to do an Earth Engine,
not that much work.
In the agriculture space, you care how well
is your vegetation doing?
How healthy is your vegetation?
And one common way of doing that is with a normalized difference
vegetation index or NDVI.
It's a pretty simple calculation.
We start with the same code that we had before
to bring that imagery in.
Define a function at NDVI, use existing Earth Engine operators
to do that calculation.
It's one band minus another divided by one
band plus another--
add the results to the image that you're working on.
And then here, you map that across all the imagery
in your collection.
Define the palette, add it to the map.
Pick what part of the map you want to show.
And in this case, I'm going to show part of Arizona
where there's some agricultural fields and hit Run.
And that'll start pulling in NDVI data
on top of some imagery data.
So what that NDVI is showing, as you get higher values that
goes to darker green.
And those dark green values register
for where areas for those lots of vegetation.
You can also move the transparency sliders
up and down to get a sense of what's behind that imagery.
You can toggle the layers on and off.
So a variety of things you can do to sort of inspect the data.
With a few more lines of code, we can do a lot more.
And here, we're, again, starting with that same basic code
that we had in the previous example,
adding a little bit of UI on top of that--
in this case, for adding a map and a label on one side
and mapping a label on the other side,
linking those two maps with a function called linker
and adding a split panel on top of that.
So with a few more lines of code, now,
you're able to go into Earth Engine
and scroll back and forth and see NDVI, and how that relates
to the imagery underneath.
So a pretty useful tool for analyzing that data.
And again, as a farmer, you might care a lot
about how that data is changing over time.
So, in this code, I've added another panel at the bottom
with this code here and added a series chart to that panel.
And so, when I click on the map, that's
going to show me the NDVI over time with that zero to one
range where NDVI is grounded.
And you can see that, generally speaking for this farmer,
overall, in 2018, the NDVI looks pretty good.
In July 18, it looks like there was probably
a cloud over the farm.
So that's why that's showing us a low number.
The last demo I have here is going to run a comparison,
just adding another time series chart,
running comparison between 2017 and 2018.
So, as that comes up, we'll have NDVI for 2017
on the left-hand side, NDVI for 2018 on the right-hand side.
And we'll start to see comparisons
between the two data sets.
And that'll start to give us an answer of,
how healthy are the fields?
How much vegetation is on those fields one year versus another?
And so, again, clicking on roughly that same spot
and looking at the results, we can
see that the NDVI in 2018 at the bottom,
here, was higher for most of the year than it was in 2017.
So this is all using data that's in the public catalog.
You can bring your own data into this, as well,
and using components that are easily
available in Earth Engine to let you inspect
that data in various ways.
So going back into the slides--
so we've spent a lot of time--
a few minutes-- in the Code Editor,
which is where we do interactive analysis.
We've started to get a simple answer for one farmer.
If you want to do that at scale, that's
where you start using Earth Engine's batch capability.
So that allows you to scale globally.
So you've got thousands of farmers
that you're serving around the world--
lets you automate runs and lets you basically
take advantage of Earth Engine's parallel computing
architecture.
And on the right, here, we've got an example
from not the agriculture space, but from work
that we did with the European JRC, Joint Research Centre,
where they wanted to attract surface water over the course
of 30 years where our service water been
changing over 30 years--
ended up being 10 million hours of computation.
We put 10,000 CPUs against it, which
allowed the computation to finish in less than two days.
So when do you really want to run a big job,
Earth Engine can help with that.
And if you want to build Earth Engine analysis into an app,
into a web page, into a mobile app,
Earth Engine APIs allow you to do that.
It gives your users the ability to customize their data,
customize the answers that they want
and basically get a lot of value and power of Earth Engine
without writing code editor code themselves.
So that's another feature where Earth Engine can
help you scale your analysis, have greater
impact with your analysis, help it
reach more and more of your users
in a way that makes sense.
So it's my last slide.
If you are interested in Earth Engine--
you think it might be able to help you solve some
of your problems faster--
you can go to earthengine.google.com,
click Sign Up.
And Earth Engine's free to evaluate.
And you can start kicking the tires,
start using the Code Editor, the APIs, or whatever makes sense
to you, and take it from there.
That's all I have.
Thank you very much for your attention--
appreciate you listening.
And, yeah, thanks for coming.
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