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Case Studies
Labelbox • Jul 1, 2021


NASA’S JET PROPULSION LABORATORY EMPLOYS ML TO FIND SIGNS OF LIFE IN OUR SOLAR
SYSTEM


 1. The ML challenge
 2. Building trust in ML
 3. Video annotation with Labelbox

Does life exist beyond our planet? Throughout recorded history, human beings
have studied the stars and wondered whether other creatures lived elsewhere in
space. Today, the search for signs of life in our solar system has focused not
on the stereotypical aliens of the human imagination — vaguely humanoid beings
or otherworldly animals — but on the one element necessary to support every
known form of life: water.

Water samples from Earth’s oceans contain billions of microbes — tiny organisms
such as bacteria and protozoa. In recent years, researchers have discovered ice
and water vapor on both Europa, a moon of Jupiter, and Enceladus, a moon of
Saturn. To further study these moons for microscopic signs of life, the Ocean
World Life Surveyor (OWLS) project at NASA’s Jet Propulsion Laboratory (JPL) is
preparing for the possibility of sending a spacecraft to one of them. The craft
will be equipped with a variety of instruments, including microscopes that will
collect video data from water samples. If the footage captured contains evidence
of microbes, NASA researchers will have effectively discovered extraterrestrial
life.

Sending samples of microscopy data back to Earth for scientists to examine,
however, is a complex task. The moons are so far from Earth that the energy cost
of sending the data back, called downlinking, is (pardon the pun) astronomical.
In fact, so little data can be returned compared to what’s gathered (<0.01%)
that it’s comparable to summarizing the entire Lord of the Rings trilogy with 57
words. Traditional compression methods simply cannot accomplish the task; for
example, downlinking the two compressed video samples shown below (40 MB in
total) would take up a third of the organization's total science data return
budget.

This is why the team in the Machine Learning Instrument Autonomy (MLIA) group
set out to build an ML model that identifies microscopy videos most likely to
contain signs of life-like motion, captures short clips of the potentially
living creatures, and prioritizes them to downlink back to Earth along with an
explanation of its decisions.


THE ML CHALLENGE

The MLIA team had four primary requirements for the onboard science autonomy:

 1. It must fit onboard a spacecraft’s computer, which is about as powerful as a
    mobile processor
 2. It should find and track any moving particles
 3. It must distinguish life-like motion from drifting or jostling using ML
 4. It must be able to efficiently explain what it saw and how it made its
    decisions

Because the amount of compute power onboard a spacecraft is highly limited, and
extracting explanations from deep learning models can be difficult, the team
decided to use traditional ML methods such as decision trees and SVMs rather
than building a deep learning model.

The recognition of life-like motion presented a complex challenge as well.
“While human beings are really good at being able to tell whether or not
something is alive by the way it moves, it’s a much more difficult task for a
computer,” said Jake Lee, a Data Scientist in the MLIA group at the Jet
Propulsion Laboratory. “Different species of microbes move differently according
to their environment. There are also free-floating particles that may move
randomly and seem organic to a computer.”

A sample from the digital holographic microscope of particles exhibiting
life-like movement. This video has been sped up by a factor of five.A video
sample, sped up by a factor of 5, where most particles are not exhibiting
life-like movement. Can you find the one motile (live) particle? Hint: Look for
the yellow dot on the right side, moving across the current.


BUILDING TRUST IN ML

The team’s model also has to gain the trust of the scientists who might put it
to use for their next mission to Europa or Enceladus. “At the end of the day,
what matters is that we work with the scientists to help them trust the model,”
said Lee. “They won’t trust it if it seems like a black box. So we are working
closely with them throughout the process and showing them how we’re training the
model. Our goal is that, when the model generates results in the end, the people
using it know how it produced those results. They know what the algorithm is
doing, and what its benefits and limitations are.”

To ensure that the model can best serve its primary users, scientists are
integrated into the ML pipeline. The MLIA team coordinates with them to review
model outputs and verify whether their model tracks the right objects, folding
their expertise into their iterative cycles throughout the process.


VIDEO ANNOTATION WITH LABELBOX

To create their algorithm, the team set about labeling a large, diverse set of
data sourced from a variety of field water samples and lab-grown specimens.
While labeling itself was an easy task, creating and maintaining an in-house
labeling solution became a challenging assignment for the small team. Because
the research team develops multiple ML projects using small, competitive grants,
they frequently have to build new labeling solutions for different labeling
tasks as swiftly as possible. The team’s PhD ML researchers were painstakingly
writing Java GUIs and manually sorting through data samples — a highly
inefficient use of their time.

To address these difficulties, the MLIA team turned to Labelbox for both the
training data platform and workforce. The team quickly uploaded video datasets
collected from Digital Holographic Microscopes. With the intuitive video editor
and basic training from the researchers, the labelers were easily able to track
various objects’ movements over time and determine if it might be a live
organism.

This sample asset has been annotated within the Labelbox video editor to track
the movement of each particle.

The Labelbox Workforce team annotated these assets quickly and enabled the MLIA
team to spend their time on other tasks, moving the project forward much faster.

> “Labelbox is so easy to use. The documentation is accessible, and the labeling
> pipeline is straightforward. We just had to upload our data, customize the
> editor to our exact requirements, and go. We had actually budgeted a week to
> get it set up, but we were done in a day.”
> 
> — Jake Lee, Data Scientist, Jet Propulsion Laboratory

Their current ML project is now on track to wrap up by the end of 2021, and will
result in an ML-powered system created from scratch that can identify, track,
and classify life-like movement directly from microscopy data. Today, the team
is working to bring other ML teams at JPL to Labelbox as well, so that the
training data platform can be a general solution for the entire lab. This ML
project is just one step for researchers planning for future space excursions —
launching a complete mission to either of these moons could require another
decade of research and development!

You can learn more about the OWLS autonomy project on the JPL website. To
discover how your ML team can benefit from a training data platform, watch this
video.

LABELBOX

TAGS Case Studies Video

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RECOMMENDED FOR YOU

Case Studies

Labelbox • Dec 11, 2018


HOW TO SCALE TRAINING DATA

A Guide to Outsourcing Without Compromising Data QualityIn order for data
science teams to outsource annotation to a managed workforce provider — also
known as a Business Process Outsourcer (BPO) — they must first have the tools
and infrastructure to store and manage their training data. Data management
tools and infrastructure should

Video

Labelbox • Nov 10, 2020


TEACH YOUR ML MODEL TO PERCEIVE TIME

At Labelbox, our mission is to build tools that teach machines how to perceive
reality and understand and predict patterns in our surroundings.

Case Studies

Labelbox • Apr 16, 2019


LABELBOX ADAPTS TO SUPPORT AMERICAN FAMILY INSURANCE AUTOMATION

In this article, we discuss why and how we built a new labeling ontology feature
to support American Family's use case. Labeling ontology is critical for machine
learning applications. It determines what the labeler can label and, in turn,
the categories the model will be able to identify.


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