- [Narrator] Innovation hinges on our ability
to see things differently.
Breaking boundaries and looking between the lines
in an effort to solve some of the world's
toughest challenges.
(upbeat music)
- Hello builder, I'm Dan Morris from the AI for Earth
program here at Microsoft.
AI for Earth's job is to put Microsoft cloud AI
tools to work for environmental sustainability.
And today my goal is to get you excited
about doing sustainability work on Azure.
AI for Earth supports the sustainability
community in three ways:
We're a grants program;
We're a data program;
And we're a technology development program.
Through our grants program, we support
over 500 organizations all over the world
with the resources they need to scale
environmental research to the cloud.
A great example of one of our grantees
using AI and machine learning
is an organization called Wild Me,
a nonprofit that makes opensource software
for wildlife population monitoring.
And in particular, Wild Me uses computer vision
to accelerate the painstaking task of identifying
individual animals in photos,
which is really important to getting accurate
population estimates for many species.
And while this is a very challenging problem
algorithmically, it's conceptually straightforward.
Here's a picture of a whale shark,
or a giraffe, or a zebra,
is this an individual animal that we've seen before?
And even if machine learning can narrow it down
to five of the thousands of animals in a database,
this can profoundly amplify scientific productivity.
For example, in the last 10 years,
Wild Me has identified more Whale Sharks
than were recorded in the entire history
of Whale Shark research before they showed up.
To date, more than 10,000 individual sharks
have been identified by over 8,000 citizen scientists,
and nearly 200 researchers, and that is a scale
that just couldn't be achieved
without vision based acceleration.
And like many of our grantees, Wild Me
makes their work available to the opensource community.
So if you wanna get involved,
check out Wild Me on GitHub.
Another organization we're proud to support
is Conservation Science Partners,
who do all kinds of amazing work
at the intersection of data science
and environmental monitoring.
One of their recent projects is another great example
of putting machine learning to work for conservation,
using their opensource framework for estimating
forest mass from aerial and satellite images.
So if processing huge volumes of images
with machine learning is your bag,
check out Conservation Science Partners' work on GitHub.
And speaking of huge volumes of images,
I mentioned that AI for Earth is also a data program.
We host the large geospatial datasets
that environmental science depends on,
which can scale into the petabytes,
especially when it comes to satellite and aerial imagery.
And if you're a Python type, we also try hard
to host developer friendly Jupiter notebooks
for each of the datasets we host,
to get you started in the right direction,
and to get you making mesmerizing pictures of the earth
in just a few lines of code.
And lastly, I mentioned that we're a technology
development program too.
Most of the work we do leverages machine learning
to help environmental scientists,
especially wildlife biologists,
efficiently process the huge volume of data
that environmental surveys generate.
Or in other words, we are here to help conservation folks
spend less time clicking on images,
and more time planning conservation.
All of our work in this area is opensource,
and our GitHub repo on processing images
from motion triggered wildlife cameras
is a great place to get involved.
We have opensource models and code in our repo,
and we also maintain a large collection
of labeled data, so you can try your hand
at training your own models too.
All of the work we do, through grants,
data hosting and technology development
comes together to put the cloud to work
for environmental sustainability,
and over the coming months you'll see this work
aggregated into a single landing point
that bundles infrastructure, tools and data
into what we call our planetary computer.
But we can't build that alone,
and we need the help of our developer community.
So please check out some of the opensource
projects we linked to today,
and we look forward to merging your PRs.
(upbeat music)
- [Narrator] Innovation hinges on our ability
to see things differently.
Breaking boundaries and looking between the lines
in an effort to solve some of the word's
toughest challenges.