In 1972,
the first Landsat satellite was launched into orbit,
ushering in a revolutionary new era of earth observation.
More than 50 years and eight satellites later,
the Landsat program has collected
an immense amount of data
that's proven an invaluable resource
to scientists
studying the complexities of our planet's surface.
The abundance of data
provides insights, but it can also pose
a daunting challenge
for researchers to extract and analyze information.
Landsat 8 and 9 alone each gather
close to a terabyte of data per day.
Enter Artificial Intelligence.
AI's popularity has taken off in recent years
with new tools
that allow users to generate imagery, transcribe audio,
and even compose music at the click of a button.
This is nothing new for the scientific community,
however, which have been using
artificial intelligence methods for decades.
When it comes
to working with Landsat data, one of the most popular A.I.
tools is machine learning.
Machine learning is a subset of AI
that can train
computer programs
to recognize patterns and analyze imagery, skills
that prove exceptionally useful
in the application of Landsat data.
In fact, when combined with Landsat
machine learning models have led
to a number of advances across a variety
of scientific fields,
granting further insight
into our planet's past, present and future.
One of the major challenges of working
with satellite imagery
like Landsat can actually be found up in the sky,
clouds
obscuring Earth's surface and casting
shadows that reduce visibility.
A cloudy day can be a downright nuisance
when it comes to analyzing certain satellite imagery.
Pinpointing these clouds helps to improve data quality
by removing noise and artifacts,
making it easier to detect changes over time.
But while accurate cloud detection
across a massive dataset such as Landsat
would be a tall task for any one
human, it's a piece of cake for a computer.
In 2019, researchers from Oregon State University
constructed a deep convolutional
neural network model
a machine learning tool that excels
at recognizing patterns in imagery.
With the help of existing Landsat 8 data,
they taught their neural network
to automatically detect clouds in satellite imagery
with an amazing 97.1% accuracy rate.
The researchers believe in the future
cloud detection algorithms
like this one could even be harnessed
to identify clouds across the entire Landsat 8 archive.
Machine learning's benefits don't just end
when the clouds clear - down on the ground
there's plenty to keep an eye on.
Our planet's one constant is change.
Earth's surface
is perpetually evolving due to human and natural forces.
Landsat its ability to track these changes over
time has proven
to be an incredible asset to the scientific community,
especially when used in concert with machine learning.
For example, researchers from the University of Texas
at Austin used Landsat data with a random force
classifier, yet another type of machine
learning tool that combines
multiple decision trees to make predictions.
Using data from Landsats 4 through 8,
They used the classifier to map changes in land use
in northwestern Belize from the 1980s to the present.
The results showed that tropical forests and wetlands
that don't have a designated protection status
are increasingly vulnerable to deforestation due
to Belize's expanding industrial agriculture.
By combining these new advances in machine
learning with Landsat
to capacity for looking back in time,
researchers believe in the future,
this approach would make it possible
to provide robust estimates of deforestation in Belize.
As climate change drives our planet's temperatures higher,
so does the
prevalence of extreme events that put ecosystems at risk.
Wildfires across
the globe have increased in frequency and intensity.
Australia is no stranger to these types of fires.
The 2019-2020 bushfire season
was one of the most destructive on record.
Using satellite imagery to pinpoint
when and where wildfires are burning
could be an important tool
for assessing damage in future fires.
Researchers at the University of Western
Australia have developed a new machine
learning approach with the help of Landsat 8 data
to generate a 16-year history of wildfire severity
in the eucalyptus forests of the continent's southwest.
They fed data gathered by satellites, including Landsat 8
from 2005 to 2020 into a supervised classifier
a type of machine learning algorithm
that learns to classify
data based on labeled examples provided during training.
By teaching the algorithm with examples from the past.
This method of machine learning could be used
to predict the severity of future wildfires.
Critical data
that could aid in the management and conservation
of Australia's extensive eucalyptus forests.
These are just a few examples of the remarkable ways
Landsat data and machine learning tools are unlocking
new possibilities for understanding our planet.
This combination has already led
to significant advances in agriculture,
forestry, urban planning, climate change
research and more
as more satellite data becomes available and machine
learning techniques continue to improve.
So too will the potential
for applications in additional fields
critical to the health of our planet's ecosystems.
In tackling
the complex challenges of today and tomorrow,
the blending of Landsat data and machine
learning will be vital to help people
make better decisions to protect our planet.
In 1972, the
Landsat mission launched its first satellite into orbit,
ushering in a revolutionary new era of earth
observation.
The Landsat mission has collected
an immense amount of data
that's proven
an invaluable resource to scientists
studying the complexities of our planet's surface.
The incredible abundance of data provides insights,
but it can also pose a daunting challenge
for researchers to extract and analyze information.
Landsat eight and nine alone, each gather
close to a terabyte of data per day.
Enter artificial intelligence
with new tools
that allow users to generate imagery, transcribe audio,
and even compose music at the click of a button.
This is nothing new for the scientific community,
however, which have been using
artificial intelligence methods for decades.
When it comes to working with Landsat data.
One of the most popular A.I.
tools is machine learning.
Machine learning is a subset of A.I.
that can train computer programs
to recognize patterns and analyze imagery skills
that prove exceptional useful
in the application of Landsat data.
In fact, when combined with Landsat
machine learning models have led to a number
of advances across a variety of scientific fields,
granting further insight into our planet's past,
present and future.
One of the major challenges of working
with satellite imagery like Landsat
can actually be found up in the sky, clouds
obscuring the Earth's surface and casting
shadows that reduce visibility.
A cloudy day can be a downright nuisance
when it comes to analyzing certain satellite imagery.
Accurately detecting these
pesky clouds would be a tall task for any one human,
but a piece of cake for a computer.
In 2019, researchers from Oregon State University
constructed a deep convolutional neural
network model
a machine learning tool
that excels at recognizing patterns in imagery.
With the help of existing Landsat eight data,
they taught their neural network
to automatically detect clouds and satellite imagery
with an amazing 97.1% accuracy rate.
The researchers believe in the future,
this machine learning technique
could even be harnessed to identify clouds
across the entire Landsat eight archive.
Machine learning benefits don't just end when the clouds
clear down on the ground.
There's plenty to keep an eye on.
Our planet's one constant is change.
Earth's surface
is perpetually evolving due to human and natural forces.
Landsat its ability to track these changes over time
has proven to be an incredible asset
to the scientific community,
especially when used in concert with machine learning.
For example, researchers from the University of Texas
at Austin used the Landsat data
with a random forest classifier,
yet another type of machine learning tool that combines
multiple decision trees to make predictions.
Using data from Landsat four through eight.
They used the classifier to map the changes in land use
in northwestern Belize from the 1980s to the present.
The results showed that tropical forests and wetlands
don't have a designated protection status
or are increasingly vulnerable to deforestation
due to Belize's expanding industrial agriculture.
By combining these new advances in machine
learning with Landsat to capacity
for looking back in time, researchers
believe this approach makes it possible
to provide robust estimates of deforestation in Belize.
Going forward,
as climate change drives our planet's temperatures
higher,
so does the prevalence
of extreme events that put ecosystems at risk.
Wildfires across the globe has seen an increase
in frequency and intensity.
Australia is no stranger to these types of fires.
The 2019
2020 bushfire season
was one of the most destructive on record.
Using satellite imagery to pinpoint
when and where wildfires are burning
can be an important tool
for assessing damage and preventing future fires.
Researchers at the University of Western Australia
to generate a 16 year history of wildfire severity
in the eucalyptus forests of the continent's southwest.
They said data gathered by
satellites, including Landsat eight from 2005 to 2020
into a supervised classifier, a type of machine
learning algorithm that learns to classify
data based on labeled examples provided during training
by teaching the algorithm with examples from the past.
This method of machine learning could be used
to predict the severity of future wildfires.
Critical data that could aid in
the management and conservation
of Australia's extensive eucalyptus forests.
These were just a few examples of the remarkable ways
the fusion of Landsat data with machine learning tools
are unlocking new possibilities
for understanding our planet.
This collaboration has already led
to significant advances in agriculture, forestry,
urban planning, climate change research and more
as satellite data such as Landsat becomes
increasingly available and machine
learning techniques continue to improve.
So too will the potential for applications
in additional fields
critical to the health of our planet's ecosystems.
In tackling
the complex challenges of today and tomorrow,
the partnership between Landsat data
and machine learning will be vital to help
people make better decisions to protect our planet.