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TANYA BIRCH: My name is Tanya Birch,
I'm a program manager at Google.
And for the past several years, I've
been working on this project called
Dynamic World, which is a near real-time land cover data set.
So just setting the stage, giving a little bit
of background on the motivation for our production of this data
set--
You know, the planet is hitting these two crises.
One is climate change, and the other one
is natural ecosystem loss and risks to biodiversity
and extinction crises.
There was this "Nature" paper in 2021 that came out that said,
for the very first time, the Amazon forest
is turning from a carbon--
It emits more carbon than it sequesters,
which is a massive and scary thing.
And we thought, OK, well, what can we do?
How can we use our digital understanding
of the physical world to catalyze environmental impact
at scale, using what Google knows about our planet?
So, in the meantime, we've been collecting data from satellites
all over the world.
And this is just the Landsat collection.
There's also Sentinel and a number of other data
sets that we were bringing on to the Google Cloud
and powering the Google Earth Engine platform with all
of this data, so pairing satellite imagery
with your algorithms to power real world applications.
And we thought, OK, well, what if there
were a way that satellite--
What if there were a satellite that produced land cover
data on the fly?
And, at the same time, around 2018,
we partnered with the National Geographic Society
to build a vision around, OK, well, what--
if we did this, who is going to use it.
And this parallel meeting, the UN Convention
on Biological Diversity is happening right now
to set some ambitious targets around protecting
30% of the planet by 2030.
So why land cover?
Land cover is used in a number of different sectors,
from water, to energy, to agriculture,
ecosystems modeling, and understanding biodiversity,
but also a number of other sectors.
And the data--
The state of the art data was data sets
like Copernicus, which, worldwide, once
a year, 100 meter resolution--
If you're in the US, you can benefit from NLCD once
every two years, and that's becoming a higher and higher
cadence.
A lot of these data sets take a lot of manual effort to create
and are, therefore, very, very precise, like NLCD.
So we started to envision Dynamic World and what
could that look like, and how would we go about building it.
And so we, again, with National Geographic Society
and their network of aerial photo interpreters,
we built a training data set.
Because AI models are, as we've heard in many talks now,
they're very data hungry.
You need a lot of hand labeled annotations
to train an AI model.
So on the top row of images, you're
looking at Sentinel-2 scenes.
The middle strip of images, you're
looking at how our annotators, expert annotators, as well
as crowd annotators, labeled the satellite imagery.
And they labeled it in a taxonomy
that was advised to us by the World Resources Institute, who
we also partnered with on the Dynamic World project.
And they looked at the Lucas data set,
they looked at the Anderson land cover labeling.
And they decided to--
They advised us to stick with this top nine land cover
taxonomy.
And then this little animation that you see here on the side
is how we trained a model, an AI model.
So how it works is, you take some label
data, water and trees, and then you feed it into an AI model.
And an AI model begins to learn and recognize
what those patterns on the landscape are.
Then you show it an unseen image.
And if you did a good job, the model really learned well,
then it makes a classification on what
the land cover would be.
And of course--
Oh, this does work.
Oh, look at that, my animation works.
So now what we're producing is, as Sentinel-2 data comes
in from the--
and comes in to the ground station,
we upload it to the Google Cloud.
And then we run it through our AI model
and produce this parallel image collection for any Landsat--
Sorry, any Sentinel-2 scene that's less than 35% cloudy.
So there is this sort of new concept
of this near real-time land cover
data set that's this parallel image
collection in Earth Engine that's
freely available to anybody.
So that's Dynamic World.
Again, just to reiterate, it's 10 meter resolution,
based on European Space Agency Sentinel-2.
Thank goodness that's freely available, because that's
something that we can then build upon
with these research efforts.
And it has near real-time, the same revisit rate as Sentinel-2
does.
It's freely and openly accessible.
They have both the AI model--
Everything that went into the production of this
is intended to be reproducible.
And our peer reviewed science paper
came out in June of this year.
So this is what the data set looks like.
This is actually an annual squash
of the data set in Ho Chi Minh City in Vietnam.
This image is, I think, one of my favorites, because it's just
incredibly beautiful.
You look at the Mekong Delta, there's
a lot of land that's used for aquaculture and rice paddies.
And you can see some of the seasonal differences,
depending on the period of time that you look at the data.
It's also a massive data set.
So last time I checked, it was over 10 petabytes
running forwards.
So it's continually running forwards.
We also ran the model backwards in time,
so the beginning of the Sentinel-2 collection,
1C collection.
And there's a new Dynamic World scene getting produced
every 14 seconds, or so.
About the accuracy, so, again, it's kind of hard to validate
the accuracy of a near real-time project that without doing--
So derivatives should really be using a design-based analysis
to understand the downstream accuracy
of a derivative product.
But we had taken 409 tiles from the annotators
and held those back for validation.
And what we're looking at is a confusion matrix
of these Dynamic World to an expert consensus
where at least two experts labeled and they all agreed.
And that's our final metric for precision, or user's accuracy,
is 73.8%.
But again, it's highly variable, right?
These are looking at top one predictions,
and I'll get into a little bit of the real true value
of this data set in a second.
We also compared against other popular or local, regional land
use, land cover maps against this 409 holdout set
and found that it performed on par
or exceeding some of the most popular land cover data sets
around.
However, this is one of the main takeaways
is it is not a static map.
If you're looking for an off the shelf static map,
you're probably better off with ESA WorldCover.
We presented with them at AGU last year.
This is really an excellent product at an annual cadence.
They have a 2021 data set that's also in the Google Earth Engine
catalog.
So I mentioned a little bit about the output
of Dynamic World, so it's per-pixel probabilities
across nine classes.
And here, we're looking at a single scene over Hungary.
So I want to spell out a little bit of what I mean.
So if I were to just take a single pixel in the center,
I can see the probability distribution
across all nine classes.
And for this specific pixel, it's
predicting trees at 23% and crops at 41%.
And that is for every single pixel for the entire Sentinel-2
catalog across these nine classes.
So where we find it can be useful is for pulling out--
This is just taking the crop value and mapping the-- seeing
the crops across--
the high probability, or the probability,
of crops across the landscape.
And this is just taking the tree signal
and mapping the "treeness" an area.
So, again, if you think about what's outside
and you imagine a 10 meter by 10 meter
block of space outside AGU, it's probably not a single label.
It's probably a combination of different land cover classes.
And so we're excited about Dynamic World
because we think that it more accurately represents
the landscape as it really is in the world.
The other thing that we did was, this is dynamic--
You can see the changes over time.
So we took that probability space
and mapped it to a hillshade effect
so that you can see the areas of high probability
pop out on the map.
But it also pulls out artifacts like this power line
that cuts through the forest.
The power line has a lower probability for trees,
so you can actually see that through the visualization
of this hillshade effect.
And that's the default way.
If you go to the Google Earth Engine data catalog for Dynamic
World, that's the default way that you
can visualize Dynamic World.
So, again, the three main innovations of Dynamic World
are this near real-time image classification
for every Sentinel-2 scene coming in; time series
class of probabilities that allow
you to look at intra-annual change,
or very timely events, like landslides, or floods,
or disasters, or things like cover crops,
if you're looking at cover crop change over time.
And then the third one is, it's really
powerful for derivative products.
Because this is where we came up with this informal slogan,
map with you, not for you.
We really want to enable the ecosystem of Earth Engine users
to take this data set as an input.
And you can even use it as an input for other training data
and train a random forest classifier
on top of that and then output a derivative map.
So here's our "Nature" scientific data paper.
We're still, knock on wood, on the home
page of "Nature" scientific data, so that makes
the URL mapping really easy.
And then you can explore the data
on a user friendly website.
Or if you want to dig deep into the time series,
I'd recommend this Dynamic World app URL at the bottom.
But it's also in the Earth Engine data
catalog, along with the ESA WorldCover data set,
and a number of other land cover data
sets, if you want to do your own analyzes or comparisons.
So this is, again, I mentioned this a second ago,
but this is my favorite way to explore Dynamic World,
is this bit.ly/dynamicworldapp because you can see--
I don't know if I have a mouse.
I do have a mouse.
OK, so you can see the time series of a single pixel.
I clicked right over here, and I can
see that in March, that pixel was classified as snow,
and later on, the rest of the year, the highest probability
is for trees.
So, with a little bit of uncertainty,
right around the area where the snow melts,
and we start to see some leaf on.
But this seasonal aspect is, I think,
one of the real values of Dynamic World.
So the last thing I want to show is an example
of how an organization, an NGO, is
using Dynamic World in an application for change
detection.
And again, change detection is a whole field unto itself, right?
They're just looking at basic pixel state
change for Amazon mining.
This is alerts.skytruth.org.
And if you go there, you can see, underneath layers,
there is Dynamic World.
And I'm going to highlight a spot in Peru, because this
is where they're looking at Amazon gold mining change
detection.
And I also--
I'm showing this example because they've
trained a custom machine learning model
to detect gold mining.
And so that provides fertile ground
to compare it against Dynamic World outputs.
And they have a baseline.
So you selected a date range, June to September 2019,
and then also 2022.
And you can filter by land classifications,
and I'm going to show bare ground, new bare ground.
Because this is the kind of Amazon mines
we detected as bare ground, or mines, not any mine, not just
in the Amazon.
And then you can define a change threshold or filter
by a threshold of change, we'll say 25% change.
And we're looking at the planet base map for high resolution
data to compare against--
to ground truth.
And this is 10% and 40%.
So you can also shift along the probability space
in the confidence in that probability.
And this is-- We're just going to stick
to 25 for this example.
And then I can also say that because there's
more uncertainty in some of these riverine areas,
I can pull out the water signal.
And the take away for this one is,
the gold mining specific ML model clearly
outperformed Dynamic World as predicting and finding,
doing change detection for gold.
But you would expect that, right?
Because it's using local data, it's refined.
It look them months to build.
What they did in Dynamic World took them about--
well, I shouldn't overestimate-- took them about a month,
or so, to put together.
But the Dynamic World data was just ready
for them to use and build these toggles upon.
So that's kind of the big thing, right?
If you want to build something that's
very, very precise and very custom,
you're better off building something that's bespoke,
using bespoke training data.
But Dynamic World can get you quite far along the way,
depending on what you're looking for.
So that's that website, again, if you
want to check it out and play with it.
I also want to just make a couple final plugs
to some resources on how to get started learning about it
and using it, beyond the paper itself.
So this is somebody in our Earth Engine community,
that is Ujaval Ghandi.
He's written three tutorials on just getting
familiarized with Dynamic World in the Google Earth Engine
environment.
And we taught a course, a free AGU workshop.
There's a lot of really fun, interactive ways that you
can explore Dynamic World in a Jupyter Notebook
in the Colab lab environment.
And you don't need technical expertise.
I promise you, you don't need to know
how to code to follow this workshop.
It's really neat, it's really fun.
You just need to get past Google authentication.
[LAUGHS]
So a couple other resources, Dynamicworld.app,
the bit.ly site that I linked to, and then a whole host
of resources for Earth Engine.
I think that's a--
The one last plug is, it's a new data set.
We're really excited to see how people will use it and hear
from the community.
So if you've started using it, or you have something
that you are building, and you're using Dynamic World,
email me, tell me about it.
I would love to hear how we can support and accelerate
your work Thank you.
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