- Up next, we're joined by Apoorva Nori
from the Azure Space team to take a first look
at the Azure Orbital preview that allows you
to connect via Microsoft and partner ground stations
to satellites around the world,
and bring Earth observational data into Azure for analytics.
Now, we're going to show you how it works
and how it fits into Microsoft strategy with Azure Space
to bring cloud connectivity everywhere on Earth,
and to make space data more accessible
for everyday use cases, like supply chain logistics
to reduce bottlenecks.
So Apoorva, thanks so much for joining us today on the show,
and congrats on our upcoming Azure Orbital preview launch.
- Thanks, Jeremy.
It's a pleasure to be here and
to share this latest milestone with everyone.
- So Azure Space was announced just
a little over a year ago.
And this included Azure Orbital,
our managed ground station as a service
as part of the Azure Space portfolio.
In fact, the upcoming Orbital preview really marks
a major step forward in the strategy.
But before we get our hands on it and show it in action,
can you share more context on Microsoft's broader focus
with Azure Space?
- Yeah, sure.
So Azure Space really marks the convergence
between the many global satellite constellations
and our cloud.
So on the one hand,
the space ecosystem is rapidly expanding
with multiple private
and public satellite operators observing
and connecting the Earth from multiple different orbits.
And then on the other hand, with Azure,
we have a massive global cloud network
with unlimited elastic compute
and expansive analytics capabilities.
And as we begin to bring the two together,
our purpose is to empower you
by bringing cloud connectivity
to even the most remote corners of Earth.
And we can connect to satellites
and harness the vast amount of data collected from space.
And just think about what this can help us with.
This can help solve both long-term trending issues affecting
the earth like climate change,
or more short-term, real-time issues.
So that could be things like connected agriculture,
monitoring and controlling wildfires,
identifying supply chain bottlenecks,
or any number of areas that
to date just may not have been possible at global scale.
- Okay, so we're taking
a comprehensive multi-satellite operator,
multi-band, and also multi-orbit approach
to make cloud connectivity and space data ubiquitous.
So how does Azure Orbital then fit into the picture?
- Yeah, so Azure Orbital is how we establish contact
between the satellite networks and our cloud.
And Azure Orbital ground station is a fully managed service.
And you saw in our graphic,
these are cloud integrated satellite systems
with giant antennas that are co-located
or directly connected with our data centers.
So here you're seeing our ground station
at the West US 2 data center in Quincy, Washington.
And this is one
of two Microsoft-owned ground stations to date.
The other one's located in Gavil, Sweden,
and we've got plenty more coming.
And we also have partner ground stations
that are integrated with Azure.
And that really expands our reach considerably.
For example, KSAT's facility in Norway provides
more than 100 antennas, and even more around the world
in both hemispheres.
And we've even begun work to integrate USEI
and ViaSat's Real-Time Earth network.
In fact, today we have Northern Hemisphere
and super Southern Hemisphere coverage.
And that's set to increase significantly
with both Microsoft and partner efforts.
- And to be clear, you know,
it's the satellite operators themselves
who Azure Orbital is really targeted for.
- That's right.
And it's made possible really by advances
over the last decade.
You know, beyond the expected government
or military-owned entities,
satellite providers are emerging more and more
from the private sector in fields like internet
and broadcast communications or aerospace.
Think of companies like SpaceX
with their Starlink internet service constellation,
or Airbus who operate the highest resolution satellites
on the market.
So where Azure Orbital comes in
is it gives satellite operators a way to access
and analyze their data more efficiently.
They can use our ground stations
or partner ground stations to uplink
and communicate with their satellites
to then downlink that data into Azure
via our global data center and networking fabric,
all without additional backhaul or ingress fees.
And, of course, from there,
they can take advantage of Azure's hyperscale compute
and analytical services to rapidly gather insights.
And also integrate with our productivity stack,
including Microsoft 365 and Teams.
- And really, you know, best of all here,
as this space data becomes more accessible,
like you mentioned earlier,
there's this tremendous downstream opportunity
then for Azure customers to use this data in the future
to solve specific business needs.
So can we see all of this in action?
- Yes, you can.
So this is different to anything else in the cloud.
The really cool part of what we're doing
is that you're literally able
to remotely control the orbital ground station
to connect to your satellite fleet
as it traverses the Earth's orbit.
Here, you're looking at the Azure Orbital portal.
And this is where as a satellite operator,
you can register a spacecraft, such as a satellite,
create a contact profile just to define what you want
to accomplish and where the data lands.
And then once you've done those steps,
you can schedule contacts
to reserve time windows on an antenna
for communicating with a satellite.
So in my case,
I've already set up a few contact profiles.
And as you can see, I've also registered a few spacecraft.
Now, if I click into one of these,
I'll use an Earth observational satellite called
Aqua from NASA.
It's a public satellite,
and it's always broadcasting on the X-band frequency.
Here I can get the contact opportunities by ground station.
Now, if I filter based on the ground station that I want,
for example, this is a Microsoft ground station,
as you can see in the GS owner column.
Now, if I filter on this ground station in Svalbard, Norway,
you'll see that it's owned by KSAT.
Now, I want to schedule a contact at this time here.
So I'll select it and hit Schedule.
And this triggers the remote movement
of an antenna at the ground station
to track the satellite as it also moves through orbit.
Now, let me explain what makes this contact possible.
The spacecraft's metadata includes its TLE,
or its two-line element,
which is used with an algorithm
to locate a satellite position and trajectory in orbit
so that the orbital antenna can point to it.
And TLE values change over time.
So orbital allows you to update this value
as often as you'd like.
And that pushes the new updated TLE
to every single antenna in the network,
including those at partner sites.
Now, this is really important
in accurately calculating the time windows
that you can schedule on each antenna per contact.
In fact, with Azure Orbital's underlying APIs,
we're handling the machine-to-machine operations for you,
as well as the data integration
with the Azure network backbone and our data services.
So this also means
that even though we're connecting via KSAT's ground station
in this case, we've set this up for minimal latency.
And again, that backhaul to Azure
from any of our partner ground stations is included.
So you don't need to pay separately for data ingress.
- Okay, so everything is configured then
to allow the ground station to connect with the satellite,
then downlink the data and pass it into Azure,
but what are some of the examples then
of the readings that we'll get now?
- Yeah, so we get a few different streams of data.
Starting in Azure Event Hub,
I've set up a namespace and a hub to listen
for ground station telemetry events
during my orbital contact.
And I have a consumer script
that's triggered by these events to grab data
and append it to my telemetry logs
as each new event comes in.
Now, I've already kicked off the script,
and it's processing the data and adding it
to our JSON file every time a value changes.
This happens to be telemetry that's coming in
from multiple satellites and multiple antennas.
So if we pause and pick this log here,
you can see the changing position of the antenna
in addition to the RF power,
which is just a measure of signal strength.
Now, all of these data points allow you
to assess how well the satellite's being tracked,
and ultimately how successful the pass is
in establishing that contact.
And you can also use this information
for planning activities for your specific mission.
So for example, you might only have a 10-minute window
in which to receive and send a finite amount of data
to the satellite.
And this telemetry can really help
you optimize your contact.
And then beyond these data points from the ground,
we can of course see the data from the satellite as well.
The Aqua satellite captures encoded weather imagery data,
which once decoded can be viewed.
And you can see an example of the output here.
You'll recognize it from weather forecast images
that you've probably seen.
And the key thing here is that all
of this is happening in one consolidated view
with one set of APIs across multiple satellite networks.
So that means if you're automating your mission,
you don't need to worry about
integrating with different networks.
- Okay, so how is this then all working under the hood?
- So there are a lot of components in motion, literally.
Remember that all of this is happening
while the Earth is rotating,
and the satellites are orbiting in various paths
and speeds and distances from Earth.
So depending on the payload,
they can switch between ground stations
to follow that satellite feed.
And we communicate with satellites
over X-, S-, and UHF-bands with even more to come.
With the connection established,
any data passed into Azure enters
over the Azure Orbital virtual network
where we process the data.
And this gives you direct connection
to our Azure network backbone.
There, in addition to operations
for antenna control and tracking that I just mentioned,
we can orchestrate multiple activities,
schedule contacts with the spacecraft,
monitor inbound telemetry,
and then decrypt and normalize inbound data feeds.
And once that data enters the Azure data center,
as an operator, you have full authority and flexibility
on what happens next.
When it comes to demodulation and decryption,
Orbital provides managed software modems
from our marketplace.
Equally, we also facilitate the transport
of the raw radio-frequency payload data
that's direct to your virtual network in Azure.
And that's where you can use software modems
that you may have already built.
- I can see that you also had DIFI, in this case,
called out as a standard.
- Yeah, that's right.
So it can be a huge interoperability challenge
when it comes to the interpreting of data coming from
different equipment providers.
So our team at Microsoft co-founded
an open standards consortium effort.
It's called DIFI,
or Digital Intermediate Frequency Interoperability,
for satellite software-defined communication.
And this is just to be able to
further virtualize the ground segment
of satellite communications,
and also remove the potential for vendor lock-in.
- Now, with the data more available,
can we dig a bit deeper into the use cases for that data?
- Yeah, so anyone can use the Aqua data,
but as you start getting into the higher resolution data,
there are so many more use cases.
For example, we have a partnership with Airbus
that has some of the highest resolution imagery available,
and that allows us to do some really powerful things.
Combining the power of Azure
with this satellite imagery ranging
from high-resolution 30 centimeters to more than 20 meters,
we can create a mapping of inventory
to identify wetlands that are at risk of carbon fixation,
decomposition, and sequestration.
Now, this data can also help provide answers
to questions like how wetlands are changing over time.
Equally, natural disasters are increasing every year,
and they can get out of control
if first responders don't have the freshest data
to assess rapidly evolving situations.
Azure, combined with the power
of high-resolution satellite data,
can serve as another set of eyes for firefighters,
identifying predictors for a wildfire anywhere
in the world from space,
and connecting first responders on the ground
with the quickest and safest escape routes.
- And so then this is going to really help, I think,
with some of the world's biggest challenges,
but what's the potential then for using space data
for maybe some more everyday practical use cases?
- Yeah, so where this can get really powerful is
when we combine Azure services like Custom Vision
as a part of Azure Cognitive Services.
For example, we're experiencing challenges today
with managing container ships and logistics
for the free flow of commerce.
So here, what you're seeing
is a basic model we've trained using aerial photography
of container ships.
And you can see that the model
is accurately assessing the container ships in frame.
But because this is from a plane or a drone,
the frame and time series data is limited
in its altitude and flight time.
So now let's scale this out to establish a way
to track thousands of ships over time
using similar machine learning models
that are running instead using satellite imagery.
So here you can see the expanded frame we can track
over time from various ports of interest.
And I can see, for example,
the levels of congestion to predict timing
to unload my shipments,
or potentially reroute ships to load balance the work
across additional ports.
Now, behind all of this,
I'm using a Jupyter Notebook that's running in VSCode.
The satellite I'm pulling data
from is the European Space Agency's Sentinel-2
Earth observation spacecraft.
We'll start off our script
by importing all of the dependencies,
which include standard data manipulation,
image processing, and some visualization libraries,
along with some Microsoft tools
for interfacing with Azure Cognitive Services
and Planetary Computer,
which is part of the Microsoft AI for Earth initiative.
Now, here, we've already trained
a computer provision prediction model,
and all you need are a set of credentials
to be able to make predictions with it.
I can use Planetary Computer's stack
or SpatioTemporal Asset Catalog APIs
to isolate some regions of interest out of the
massive corpus of data that we've collected
over time from Sentinel-2.
And I'm even filtering here on image quality parameters
such as cloud coverage.
Now, once I've narrowed our search,
I can now call Cognitive Services
to identify ships and their locations
within these Earth observational images.
All we're doing in this cell is using our predictor,
making predictions in our images,
and logging some of the information like
where the ships are,
and what the model's confidence
in identifying those ships is.
Now let's take a look at some of the images
that we've made predictions on,
just to get a feel for what the model's doing.
Here, we can see an object
that the model is quite confident is a ship.
And then here's a prediction with much lower accuracy,
and it's clearly not a ship.
So now that we've run all of our images
through the ship detection model,
we can start to do some analysis
on historic trends within this data.
And since we're running a forecasting model,
we'll want to pull in as much data as possible.
So I've expanded our Sentinel data set
to stretch back as far as 2015.
And I also want to filter out
low-confidence model predictions,
so we can more accurately visualize the number of ships
in the LA Port harbor over time.
Once we have this data,
we can start to do standard analysis on time series data
by applying techniques like anomaly detection
and forecasting.
And here, we're building a really rough forecast
just to see what kinds of volumes we might anticipate
in the future.
While this isn't a perfect forecast;
ideally we'd want to do much more validation on this.
We can use it to look at what
this preliminary model learned, just to get a high-level
sense of some of the patterns in this data set.
For example, the forecast breakdown shows
a slowly increasing trend in the number of ships,
with a strong seasonality component
where the number of ships are significantly lower
in the months of May and June,
but higher in September in October.
So what we've essentially done here is take large amounts
of Earth observational data,
and then created time series data out of it
to drive these insights and predictions.
- Nice, and I really love the example in this case
because we're using the space data
to address a topical problem, I think,
that we're all feeling the effects of today.
- Yeah, thanks. And that's just one example, Jeremy.
With Azure Space and our partner ecosystem
of geospatial and data analytics providers like Airbus,
Esri, Blackshark, and Orbital Insight,
we're making the data more accessible for Azure customers.
And also, on the cloud connectivity side with Azure Orbital,
we have the ultimate cloud-enabled edge devices both on
and now off the planet.
And you can imagine what scenarios this can light up
from self-driving cars to media content distribution,
and so much more.
- Truly groundbreaking stuff.
So what's next, and where can people watching learn more?
- So whether you're a space enthusiast
or an organization with a spacecraft
that wants to find out more,
you can visit aka.ms/orbital.
- Thanks so much for doing this today, Apoorva.
And also, keep checking back to Microsoft Mechanics
for all the latest updates.
And be sure to subscribe if you haven't already.
And thank you so much for watching.
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