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>> Okay, welcome back everyone
to theCube's live coverage here in Las Vegas
for Amazon re:MARS two days of coverage.
We're getting down to wrapping up day one.
I'm John Furrier, host of theCube.
Space is a big topic here.
You got Machine Learning.
You got automation Robotics.
All spells MARS.
I've two great guests here to really
get into the whole geo scene,
and what's going on with the data.
We've got Marcus Norrgren,
Business Development in Geo Data, Sogeti,
Part of Capgemini Group,
and Joakim Wahlqvist,
Portfolio Lead, Data and AI,
with Sogeti, Part of Capgemini.
Gentlemen, thanks for coming on theCube.
Appreciate it.
>> Thanks for having us. >> Glad to be here.
>> So coming all the way from Sweden
to check out the scene here
and get into the weeds and the show.
A lot of great technology here.
Space is the top line here, but software drives it.
You got Robotics.
Lot of satellite.
You got the aerospace industry colliding with hardcore
industrial, I say IOT, robotics,
you want to call it whatever you want,
but space kind of highlights the IOT opportunity.
There is no edge in space, right?
So the edge, the intelligent edge,
a lot going on in space, and satellite's one of them.
You guys are in the middle of that.
What are you guys working on?
What's the focus here for Capgemini,
Sogeti, Part of Capgemini?
>> Joakim: I would say we focus
a lot to creating business value,
real business value for our clients
with the satellites available,
actually freely available satellite images
working five years now with this solutioning,
and mostly in vegetation management and forestry.
That's our main focus.
>> So what's the product value you guys are offering?
>> We basically,
for now the most value we created is
working with a forest client to find
bark beetle infestations in Spruce forest.
It's a big problem in European Union
and Northern region of Sweden where we live.
Now with the climate change, it's getting warmer.
The bark beetle, they swarm more times during the summer
which makes it spread exponentially.
So we help with the satellite images
together with data science and AI
to find these infestations in time
when they are small, before it spread.
>> So satellite imagery combined with data,
this is the intersection of the data piece,
the geo data, right?
>> Marcus: Yeah, you can say that.
You have a lot of open satellite data
and you want to analyze that,
but you also need to know what you're looking for,
and you need data to understand,
in our case, a certain type of damage.
So we have large data sets that we have to sort of clean
and train ML models on
to try to run that on that open data to detect these models.
And when we're saying satellite data and open data,
it's basically one pixel is 10 by 10 meters.
So it's not that you will see the trees.
>> John: Yeah.
>> But we're looking at the spectral information
in the image and finding patterns.
So we can actually detect
attacks that are like four or five trees big
using that type,
and we can do that throughout the season
so we can see how
you start seeing one, two attacks and it's just growing,
and then you have this big area of just damage.
>> John: So how long does that take?
Give me some scope to scale because it sounds easy.
Oh, the satellites are looking down on us.
It's not.
It's a lot of data there.
What's the complexity.
What are the challenges that you guys are overcoming,
scope to scale?
>> Joakim: It's so much complexity in this.
First, you have clouds.
So it's open data set,
you download it and you figure out,
here we have a satellite scene, which is cloudy.
We need to have some analytics doing that,
taking that image away basically,
or the section of the image which is cloudy.
Then we have a cloud free image.
We can't see anything because it's blurry.
It's too low resolution.
So we need to stack them on top of each other.
And then we have a next problem,
to correlate them so they are pixel perfect overlapping.
>> John: Yeah.
>> So we can compare them in time.
And then they have the histogram adjustment to make them,
like the sensitivity is the same on all the images
because you have solar storms,
you have shady clouds
which could be used to steal that image.
So we need to compare that.
Then we have the ground proof data
coming from a harvester for instance,
we got 200,000 data points from the harvester,
real data points where they had found bark beetle trees
and they pulled them down.
The GPS is drifting 50 meters.
So you have an uncertainty
where the actually harvest it was.
And then we had the crane on 20 meters.
So, you know, the GPS is on the home actual machine
and the crane were somewhere.
So you don't really know.
You have this uncertainty.
>> John: It's a data integration problem.
>> Yeah.
>> John: Massive
>> A lot of interesting things to adjust for.
And then you could combine this into one deep learning model
and build the solution.
>> But on top of that, I don't know if you said that,
but you also get the data in the winter,
and you have the problem during the summer.
So we actually have to move back in time
to find the problem, label the data,
and then we can start identifying.
>> So once you get all that heavy lifting done,
or write the code,
or I don't know if something's going on there,
we get the layering, the pixel pack,
see all the how complex that is,
when the deep learning takes over,
what happens next?
Is it scale?
Is all the heavy lifting upfront?
Is the work done upfront or?
Is it it scale on the back end?
>> Joakim: So first the coding is heavy work, right?
To gets hands on and try different things,
figure out in math how to work with this uncertainty
and get everything solved.
Then you put it into a deep learning model to train that.
It actually run for 10 days
before it was accurate.
First installation, it wasn't accurate enough.
So we scrap that, did some changes,
then we run it again for 10 days.
Then we have a model which we could use
and interfere new images,
like every day, pretty quickly.
Every day it comes a new image.
We run it.
We have a new outcome,
and we could deliver that to clients.
>> John: Yeah, I can almost imagine,
I mean, the cloud computing comes in handy here.
>> Oh yeah.
>> So take me through the benefits because
it sounds like the the old expression,
"the juice is not worth the squeeze."
Here it is,
it's worth the squeeze if you can get it right.
'Cause the alternative is what?
More expensive gear,
different windows,
just more expensive monolithic solutions right?
>> Joakim: Think about the data here.
So it's satellite scene.
Every satellite scene is 100 by 100 kilometers.
That pretty much right?
And then you need
a lot of these satellite scene over multiple years
to combine it.
So if you should do this over the whole Northern Europe
over the whole globe, it's a lot of data.
Just to store that, it's a problem.
You cannot do it on-prem,
and then you should compute it with deep learning models.
It's a hard problem if you don't have-
>> So you guys got a lot going on.
So talk about Sogeti Part of Capgemini.
Explain that relationship,
'cause you're here at a show that, you know,
you got, I can see the Capgemini angle.
This is like a little division.
Is it a group?
Are you guys like lone wolves?
Like, what's it like,
is this dedicated purpose built focus around aerospace?
>> No, it's actually,
Sogeti was the name of the Capgemini company
from the beginning.
And they relaunched the brand 2001 I think,
roughly 20 years ago.
So we actually celebrate some anniversary now,
and it's a brand which is more local,
close to clients out in different cities.
And we're also tech companies.
We are very close to the new technology, trying things out.
And this is a perfect example of this.
It was a crazy idea five years ago, 2017,
and we started to bring in some clients,
explore, really open-minded,
see, can we do something on these satellite data?
And then we took it step by step together with our clients.
>> John: Yeah.
>> Joakim: And it's a small team.
We're like 12 people today. >> John: Yeah.
And you guys are doing business development.
So you have to go out there and identify
the kinds of problems that match the scope of the scale.
>> So what we're doing is we interact with our clients,
do some simple workshops or something,
and try to identify like the really valuable problems
like this Spruce bark beetle.
That's one of those.
Yeah.
And then we have to sort of look at,
do we think we can do something?
Is it realistic?
And we will not be able to answer that 100%,
because then there's no innovation in this at all,
but we say, well, we think we can do it.
This will be a hard problem, but we do think we can do it.
And then we basically just go for it.
And we, this one we did in 11 to 12 weeks.
A tightly focused team
and just went at it.
Super slim process, and we got the job done
and the results-
>> John: Well, it's interesting you have a lot of use cases.
We got to go down, do that face to face, belly to belly,
you know, body to body sales Biz Dev,
scoping out have workshops.
Now this market here, re:MARS,
they're all basically saying, call to arm,
more money is coming in.
The problems are putting on the table,
the workshop could be a launch meeting, right?
I mean, because earth emission
is a big set of problems to tackle.
>> Marcus: Yes.
>> So,
I mean, I'm just oversimplifying,
but that being said,
there's a lot going on opportunity wise here,
>> Joakim: Yeah.
>> That's not as slow maybe as the the Biz Dev at, you know,
coming in, this is like a huge demand.
>> Joakim: It will be explode.
>> What's your take on the demand here,
the problems that need to be solved,
and what you guys are going to bring to bear for the problem?
>> So now we have been focusing
mainly in vegetation management and forestry,
but vegetation management
can be applicable in utility as well.
And we actually went there first.
Had some struggle
because it's quite detailed information that's needed.
So we backed out a bit into vegetation in forestry again,
but still it's a lot of application in utility,
and vegetation management in utility.
Then we have a whole sustainability angle.
Think about auditing of rogue harvesting,
or carbon offsetting,
in the future even biodiversity offsetting.
That could be used-
>> John: And just to point out
and give a little extra context,
all the keynotes talk about space
as a global climate solution potentially,
the discoveries and or also the imagery they're going to get.
So you kind of got, you know,
top down bottoms up if you want to look at the world as bottom
and space kind of coming together.
This is going to open up
new kinds of opportunities for you guys.
What's the conversation like when this is going on?
You're like, oh yeah, let's go in.
Like, what are you guys going to do?
What's the plan?
Going to hang around and ride that wave?
>> I think it's all boils down to
finding that use case that need to be solved
because now we understand the satellite scene,
they are there.
We could,
there is so many new satellites coming up,
already available when you come up,
the cloud platform AWS, it's great.
We have all the capabilities needed.
We have AI, ML models needed, data science skills.
Now it's finding the use cases together with the clients,
and actually deliver on them one by one.
>> John: It's interesting.
I'd like to get your reaction to this Marcus too as well.
What you guys are kind of,
you have a lot bigger
and bigger than some of the startups out there,
but the startup world, they find their niches,
and the workflows become the intellectual property.
So this, your techniques of layering,
I almost see is an advantage out there.
What's you guys view of that
on intellectual property of the future?
Open Source is going to run all the software.
We know that.
So software's going Open Source.
Scale and integration,
and then new kinds of ways are new methods.
I won't say for just patents,
but like just for intellectual property differentiation.
How do you guys see this as you look at this new frontier
of intellectual property?
>> That's,
it's a difficult question.
I think it's,
there's a lot of potential
if you look at open innovation
and how you can build some IP which you can out license
and some you utilize yourself,
then you can build like a layer business model on top
so you can find different channels.
Some markets, we will not go for.
Maybe some of our models
actually could be used by others where we won't go.
So we want to build some IP,
but I think we also want to be able to release
some of the things we do.
>> John: Open works.
>> Yeah, because it's also builds presence.
>> Community.
>> Yeah, exactly,
because this problem is really hard
because it's a global thing.
And it's,
imagine if you have a couple of million acres of forest,
and you just don't go out walking
and trying to check what's going on
because it's, you know, that's,
>> John: Manual's hard.
>> Yeah. >> Joakim: It's impossible.
>> So you need this to scale,
and it's a hard problem.
So I think you need to build a community.
>> Yeah.
>> Because this is,
it's a living organism that we're trying to monitor
if you talk about the vegetation of the forest.
It's changing throughout the year.
So if you look at Spring,
and then you look at summer,
and you look at Winter,
it's completely different what you see.
>> John: Yeah yeah.
So it's interesting,
And so, you know, I wonder if, you know,
you see some of these crowdsourcing models
around participation, you know, small little help,
but that doesn't solve the big puzzle,
but you have Open Source concepts.
We had Anna on earlier,
she's from the Amazon sustainability data project.
>> Marcus: Yeah, exactly.
>> And this just like open up the data.
So that's data party for her.
So in a way there's more innovation coming potentially,
if you can get that thing going, right?
Get the projects going.
>> Joakim: Exactly.
And all this, actually our work is started because of that.
>> Marcus: Yes, exactly.
>> So European Space Agency,
they decided to hand out this Copernicus program
and the Sentinel Satellites
Sentinel one and two which we have been working with.
They are freely available.
It started back in 2016, I think.
>> John: Yeah.
>> And because of that,
that's why we have this work done during several years.
Without that data freely available,
it wouldn't have happened.
>> Yeah. >> I'm pretty sure.
>> Well, what's next for you guys?
Tell me what's happening.
Here's the update.
Put a plug in for the group.
What are you working on now?
What are you guys looking to accomplish?
Take a minute to put a plug in for the opportunity.
>> I would say scaling this.
Scaling, moving outside Sweden of course.
We see our model that they work in in US.
We have tried them in Canada.
We see that where we work.
We need to scale
and do field validation in different regions.
And then I would say,
go to the sustainability area of this,
because there, there is a lot of great potential.
>> John: International too is huge.
>> One area I think that is really interesting
is the combination of understanding the,
like the carbon sink and the sequestration
and trying to measure that,
but also on top of that,
trying to classify certain Keystone species habitats
to understand if they have any space to live,
and how can we help that to sort of grow back again.
Understanding the history of the sort of the force.
You have some data online,
but trying to map out how much of this
has been turned into agricultural fields for example.
How much of the real old forest we have left
that is really biodiverse?
How much is just eight years young?
To understand that portion,
how can we sort of move back
towards that blueprint we probably need to.
>> John: Yeah.
>> And how can we digitize and change forestry
and the business models around that
because you you can do it in a different way,
or you can do both some harvesting,
but also, not sort of ruining the whole ecosystem.
>> They could be more efficient.
You make it more productive,
save some capital, reinvest it in better ways.
>> Marcus: And you have robotics.
And that's maybe something that we are not so active in,
but I mean, starting to look at
how can autonomy help forestry,
inventory damages, flying over using drones and satellites.
You have people looking into autonomous harvesting of trees
which is kind of insane as well,
because they're pretty big,
but this is also happening.
So I mean, what we're seeing here is basically-
>> I mean, I read a story multiple times
called 'On Sale Drones', one of my favorite stories.
The drones that are just like
getting bobbed around in the ocean,
and they're getting great telemetry data
'cause they're indestructible.
They can just bounce around,
and then they just transmit data.
>> Joakim: Exactly.
>> You guys are creating a opportunity,
some will say problem,
but by opening up data you're actually exposing
opportunities that never have been seen before,
because you're like it's that scene where,
that movie Jody Frost a contact where
open up one little piece of information,
and now you're seeing a bunch of new information.
You know, you look at this large scale data,
that's going to open up new opportunities to solve problems
that were never seen before.
>> Exactly.
>> You can't automate what you can't see.
>> No. >> Right?
That's the thing.
>> We haven't even thought that
these problems can be solved.
It is basically, this is how the world works now.
Because, before, when you did remote sensing,
you need to be out there.
You need to fly with a helicopter,
or you put your boots on out and go out.
Now you don't need that anymore.
>> John: Yeah.
>> Which opened up that you could be-
>> John: You can move your creativity to another problem.
Now you open up another problem space.
So again, I like the problem solving vibe of the,
it's not like, oh, catastrophic.
Well, the earth is on a catastrophic trajectory.
It's like, all will agree to that,
but it's not a done deal yet.
Got plenty of time, right?
So like, but let's get these problems on the table.
>> Yeah. >> Yeah.
>> And I think this is the new method.
Well, thanks so much for coming on theCube.
Really appreciate the conversation.
>> Thanks a lot.
>> I love it.
Opening up new world opportunities, challenges.
There's always opportunities when you have challenges.
You guys are in the middle of it.
Thanks for coming on.
I appreciate it. >> Thank you.
Thanks guys.
Okay.
Capgemini in theCube, Part of Capgemini,
Sogeti Part of Capgemini here in theCube.
I'm John Furrier, the host.
We'll be right back with more after the short break.
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