ENHANCE DISASTER PREPARATION
AND RESPONSE WITH REMOTESENSING TECHNOLOGIES
Industry PerspectiveADVANCES IN DATA ANALYSIS,MANAGEMENT AND MACHINE LEARNING
ARE IMPROVING DISASTER MANAGEMENT
The recent proliferation of remote sensing platforms has presented those who prepare, respond and help
communities recover from natural disasters with unprecedented opportunities and challenges. Accurate, unbiased
situational awareness information is available faster than ever. But the enormous volume of raw data is creating new
problems related to the timely analysis, extraction and dissemination of useful information. L3Harris has committed
to creating product and service solutions that put actionable intelligence into the hands of disaster managers both
before and after a catastrophic event occurs.

On the evening of August 4, 2020, a
Beirut warehouse filled with ammonium
nitrate exploded with enough force toobliterate the entire port facility anddamage buildings six miles away. There
was an immediate scramble of activity
to try and get intelligence as to what
happened and what the effects were.
Remarkably, just hours later in thedark of night, the European Sentinel-1
satellite captured synthetic apertureradar (SAR) data of the scene providing
disaster response officials their first
synoptic overview of the destruction.

“Effective disaster response starts before the event occurs – inthe planning, preparation and prevention phases...this is where
remote sensing and geospatial technologies are increasingly
being applied,” said O’Connor.

The Beirut explosion illustrates thevalue of satellite remote sensing as a
critical source of information for disastermanagement. Since the 1970s and 80s,
we have relied on Landsat and SPOT
imagery to provide coarse-resolution
views of landscapes devastated bydisasters like floods, oil spills, tornadoes
and wildfires. Most of these data sets,
however, were collected days or weeks
after the event due to infrequent revisit
cycles of the satellites, usually limiting
their applications to the clean-up andrecovery phase of disaster response.

The transition of geospatial technology
from the disaster recovery phasetothe emergency preparation andresponse phase is a huge achievement,
according to Amanda O’Connor, Industry
Principal at L3Harris Geospatial.
But it’s just part of the story.

In the past several years, however,the use of imaging satellites and othergeospatial technologies has evolved
to support emergency response in theimmediate aftermath of a disaster, notweeks later – an enormous leap forward.
For response agencies, speed is of theessence, and situational awareness iskey. They need to know which areas
were hit the hardest so they can deploy
search-and-rescue personnel alongwith water, food and medical aid in aprioritized manner. Remote sensing
platforms continue to enhance thekinds of information that can impact
mitigation, preparation, response andrecovery for all types of disasters.
We are living in a Golden Age of remotesensing where at least one of a plethoraof new imaging satellites is likely to passover a trouble spot within hours of an

unplanned event. And more importantly,that satellite will probably be carrying
the right sensor for the job. As witnessedin Beirut, Sentinel-1 is equipped with a
SAR sensor capable of seeing throughdarkness and lingering smoke, somethingan optical satellite could not accomplish.

NEW TECHNOLOGY: OPPORTUNITIES AND CHALLENGES
While satellites play a glamorous role in this Golden Age, they are accompanied by a supporting cast ofmanned and unmanned aircraft capturing highly detailed data in localized situations. Airborne sensors have
evolved right along with spaceborne devices, collecting diverse data sets faster and more economically thanever before. Even on the ground, land-based sensors, part of the emerging Internet of Things, are enabling
better emergency management and response.
The abundance of sensors has been a net positive for disaster planning and situationalawareness, but it has also created new challenges related to dealing with all of thisdata. As sensor platforms have multiplied and their collected data files have becomelarger and more complex, incident commanders and others in the emergency response
chain face at least three distinct challenges:

Fortunately, L3Harris Geospatial has
developed a series of products andservices to streamline the querying,
retrieval, exploitation and disseminationof actional intelligence for disaster
preparation and response.

> Find the right data for a particular situation

Several geospatial data collection
technologies are the main drivers behindthe unprecedented opportunities andchallenges facing the disastermanagement community.

> Analyze raw data to extract situational information> Deliver actionable information to the front lines

SMALLER SATELLITES
The miniaturization of propulsion,
stabilization and power subsystems has
reduced the size of satellites to the point
where entire constellations can be built
and launched for less than the cost of asingle legacy platform like Landsat and
SPOT. Multi-satellite constellations havebecome the norm for new commercial
Earth observation systems.
The upside to so many satellites inorbit is that nearly every spot on the
globe can be imaged several times
throughout the day. This means thedestruction from an explosion, storm or
earthquake can usually be mapped within
hours. Even more importantly, multiple
satellite passes throughout the day andfollowing days means dynamic ongoingincidents like floods and fires can betracked continuously.

As noted, the downside is that the enormous volumes of data being captured so quickly
from different types of sensors make it difficult to find and analyze the right image for agiven situation.

ADVANCED OPTICAL SENSORS
Imaging sensors have gotten smaller, less expensive and more sophisticated allowing them to fly on small satellites, aircraft anddrones. While optical sensors by their nature can operate only in daylight, the major technical improvement is spatial resolution,
which enables details as small as 30 cm to be seen from space and objects just a few centimeters to be discerned from the sky.
Even minor damage to infrastructure and buildings can be mapped accurately in the wake of a disaster – think being able to detecta large rope tethered to a sunken boat. The advantages of higher spatial resolution are infinite, but the downside is enormous files
that are difficult to archive and search.

SPECIALTY SENSORSPROFESSIONAL UAVS

The two most important specialty sensors
being deployed for disaster management
today are Hyperspectral and Thermal
Infrared scanners.

The cost of unmanned aerial vehicles(UAVs), or drones, with the stability andpower to carry mapping-grade imaging
sensors and laser scanners (LiDAR) has
transformed the remote sensing industry.
UAVs are now performing imaging andelevation mapping missions over small
areas of interest at a cost and speed not
possible for manned aircraft.

The wait for commercial hyperspectral
sensors has been long, but they are
now being flown on satellites, manned
space craft like the International Space
Station (ISS), aircraft and drones. Thesepassive instruments measure reflectedenergy typically in 30 or more discrete
wavelengths spanning the visible (red,green, blue), near infrared and short-wave
infrared. This data contains rich, often
invisible insights into objects on the
ground, especially vegetation, allowing for
subtle differentiation by type or condition.
In disaster management, hyperspectral
imagery is now being used to classify
land cover vegetation in areas prone
to wildfires. Some species burnmore readily than others, and thishelps planners map sites requiring
mitigation. Additionally, FEMA has begun
experimenting with hyperspectral data toclassify types of post-storm debris piles
to facilitate clean-up efforts. The main
challenge of this valuable data today
is that it is still so new few geospatial
professionals have expertisein processing it.
The applications of thermal sensors,
mostly flown on manned aircraft anddrones, are more straight forward. These
optical instruments measure emissionsof heat from sources. Today, their mostcommon application is to map hot spots
and perimeters of wildfires. A significant
advantage is their ability to capture thermal
data even through heavy smoke.

SYNTHETIC APERTURE RADAR (SAR)
Unlike passive optical sensors, SAR
systems have their own power sources
allowing them to capture data day or night,
through clouds, light smoke and in mostweather conditions. For decades, thepower required to operate these systems
limited their use to large satellites oraircraft. In just the past two years,
technology breakthroughs have put SAR
sensors in small satellites – most notably
the ICEYE and Capella systems – and insmaller manned aircraft.
Active all-weather imaging makes SAR
ideal for capturing disaster information atnighttime, during disasters that cause large
amounts of smoke like fires or volcanoes,in cloudy equatorial regions and in polar
darkness. Radar also precisely delineateswater/land boundaries, providing
extremely accurate flood extent mapping.
It can also pinpoint oil spills on water. On
land, SAR can mitigate geohazards related
to ground movement by spotting minuteshifts in landform positions and elevations.
The traditional challenge for operational
SAR use has been the complexity ofthe data which historically required
expensive and complicated processing
software that even the most experienced
geospatial professionals were not trained
to utilize. The data and tools are now more
approachable to the geospatial community.

For disaster applications, they can fly
much closer to the surface of objects,
getting incredibly detailed views ofdamage, often from many perspectives.
In addition, some drones are so
inexpensive they are considereddisposable, capturing data in conditions
too hazardous for manned aircraft.
In terms of disaster mitigation, LiDARequipped drones are increasingly being
used in powerline corridors to monitor
vegetation encroachment that could
spark a fire.
The current challenge for UAV use inchaotic situations is the lack of standards
for data collection. Multiple drones may
be flying over a site simultaneously, each
capturing data at a different resolution
from a different altitude. Syncing such avariety of data sets for a consistent viewof the situation is a major challenge.
Complementing these improvements
in data collection are equally criticalbreakthroughs in data processing andexploitation driven by advances incomputer power, automation of image
processing algorithms and the integrationof Artificial Intelligence into workflows.

MANAGING THE DATA TSUNAMI
For decades, L3Harris has developed solutions for the U.S. military, and has been at the front lines ofaddressing challenges of enormous volumes of raw data coming in too fast for it to be processed, analyzed
and digested by commanders. In the last decade, L3Harris has pivoted to customizing many of these samedata management remedies as solutions to similar problems facing civilian and private sector organizations.
As noted, the overarching data
management problem in chaotic disaster
scenarios is finding the right data set,
processing it into understandable
information and disseminating that
information to responders in the field.
To accomplish data query and informationdelivery, L3Harris Geospatial offers
Jagwire™, a web-based software productproviding an end-to-end geospatial data
management solution and delivering nearreal-time search and discovery, image
exploitation and information dissemination.
Jagwire is capable of ingesting enormousvolumes of incoming geospatial data,
including live video feeds, satellite imagery,
LiDAR point clouds and GIS vector layers.
“Jagwire is designed to deliver imagery
and other geospatial files so they are
understandable to personnel with no GIS
or image processing training,” said Joey
Griebel, L3Harris North American SalesManager. “Even different types of video or
imagery streamed in real time from a UAV
can be presented as a common operating
picture to many incident commanders at
different locations.”

During multiple California wildfires in the summer of 2020, L3Harris supported fire
perimeter mapping activities. Throughout the disasters, the CA Air National Guard
flew drones equipped with thermal infrared video cameras. These video feeds were
downlinked to a third-party processing hub on the ground that applied AI algorithms to
extract wildfire perimeters from the thermal infrared data. In seconds, this information
was transmitted to Jagwire, which streamed the annotated videos to mobile devices in
the field and laptop computers in other incident command centers.

SIMPLIFYING INFORMATION EXTRACTION
Data management is critical, but it is only the first step in dealing with the torrent of incoming geospatial data.
The next step is extracting meaningful information from the wide variety of complex data sets now available.
ENVI Modeler is frequently used in disaster
preparation to set up complex information
extraction workflows to be performed
repeatedly in an automated fashion as
new imagery is collected and received. The
workflow, for example, could continuously
monitor vegetative class changes likegrasses to shrubbery in hyperspectral
imagery and trigger a warning when certain
dangerous thresholds are exceeded.

L3Harris offers an information extraction
solution in the form of its commerciallyavailable ENVI® software. ENVI is a robust
geospatial data analysis software thatallows anyone with minimal training to
extract meaningful information from alltypes of imagery – optical, multispectral,
hyperspectral, thermal, LiDAR and SAR.
“ENVI users don’t have to be remotesensing scientists or big data analysis
experts to derive meaningful informationfrom raw data sets,” said O’Connor.
L3Harris has developed science-based
image analysis algorithms and integrated
them into automated workflows in ENVI.
This enables users to derive answers to
the most complicated geospatial queries
from enormous data sets with a few
clicks of the mouse. Moreover, ENVI gives
more experienced users the means tocreate algorithms and build customized
automated analysis processes of their own
via creating visual models or coding.
To facilitate certain image analysis
applications, ENVI bundles complex
workflows into more than 200 automatedmodules or tools (often called ‘Tasks’).
These span many functional areas
including georeferencing, calibration,
spectral analysis, morphological filters,
classification, object detection, terrain
modeling, point cloud analysis, SAR
analysis, time series operations, machinelearning / deep learning and many more.

Change detection is among the disastermanager’s most powerful tools. Images
acquired over the same area before andafter an event are loaded into ENVI,
and with the push of a few buttons, thesoftware identifies where change – ordamage – has occurred over time.
The ENVI Feature Extraction module
puts the power of the computer to work
identifying features based on their spatial,
spectral and textural characteristics.
Rivers, lakes, fields, trees and coastlines
can automatically be mapped.
ENVI feature extraction algorithms
have become so sophisticated, theycan even differentiate objects by theircondition in multispectral imagery. For
example, stressed plants can be isolated
automatically from healthy ones in farm
fields or forests. This information is used tofind and remediate disease or infestation
before they expand into widespread crop
failures which can wreak havoc on foodsecurity and economic stability.

In addition, ENVI is easily extendable via its
scripting language, IDL®. This enables ENVI
users to create customized algorithms,
workflows and analytics that suit theirneeds. For example, NASA techniciansused IDL to create 3D visualizations ofrain and snowfall data captured by its
Global Precipitation Measurement (GPM)
mission. GPM gathers precipitation
data from several satellites carrying
radar instruments for use in a variety of
applications. Among the most importantis modeling precipitation volumes in nearreal time as a predictor of dangerous
flooding and related rainfall events.

AUTOMATING ANALYTICS WITH DEEP LEARNING
The identification of specific objects
in data sets has been greatly facilitatedby automated recognition with the
ENVI FX module. There are numerous
other features specific to identifying
the destruction and damage such as
spectral, textural and special qualities
related to various catastrophes thatdisaster managers need to find andmap in remotely sensed data sets.
L3Harris has a solution for this challenge
as well – ENVI Deep Learning. A formof Artificial Intelligence, Deep Learning,enables the user to ‘teach’ the software
how to recognize nearly any feature orcondition in the data by its spectral,
situational and neighboring features.
Where earlier forms of Machine Learningalgorithms required the user to ‘show’ thecomputer hundreds of examples of thefeature to be identified, Deep Learning
uses iterative algorithms that can learn
from just a dozen examples in some cases.
“As with the other ENVI modules, you
don’t have to be an Artificial Intelligenceexpert or programmer to use the ENVI
Deep Learning module,” said Griebel.
“The module has intuitive tools andworkflows that enable users to easilylabel data and generate models with theclick of a button.”
Disaster planners have embraced the
ENVI Deep Learning module for thesignificant savings in time it provides
them, compared with manual processes.

“Finding and counting all the damaged houses, and differentiatingthem from rubble piles, in the imagery would have taken days
with the naked eye” said Griebel.
In the aftermath of a March 2020 tornado that tore through a suburb of Nashville, Tenn.,
L3Harris obtained aerial imagery collected after the deadly incident. The image resolution
was sufficient to clearly identify damage to homes and other structures. In less than
two hours, a technician used the ENVI Deep Learning module to teach the computer
to find damaged structures on its own. The actual mapping of destruction in theimagery took just a few minutes. “Finding and counting all the damaged houses, anddifferentiating them from rubble piles, in the imagery would have taken days with the
naked eye,” said Griebel.

EXTRACTING INFORMATION FROM
RADAR AND HYPERSPECTRAL DATA
Image data sets are becoming more complex, and SAR and Hyperspectral are the two leading examples.
Their size makes them particularly difficult to automate and exploit. Again, L3Harris has leveraged the analyticalcapabilities in ENVI to automate the information extraction functions from these two data sets.
To make exploiting SAR data more user
friendly, L3Harris has introduced ENVI
SARscape® Analytics, an analysis modulethat integrates multiple processing functions
and algorithms into simplified, automatedworkflows. As with the other ENVI modules,
ENVI SARscape puts powerful scientificcapabilities into the hands of end users
to quickly and easily extract meaningful
results from SAR data sets.
In the Beirut port explosion cited earlier,
L3Harris technicians used ENVI SARscapeAnalytics to process the Sentinel-1 satellite
SAR data and then overlay it on an archived
optical image of the same area to performa change detection analysis. The result wasa detailed map highlighting any feature that
had been altered by the blast, including
structural damage or covering by debris.

tradition of creating simplified push button
workflows for hyperspectral data sets.

Read the full Beirut case study >> Anomaly Detection

With hyperspectral imagery more readily
available thanks to new sources of data
(Satellites, ISS hosted systems, aerial
and drone systems), disaster planners
and managers have greater access to
the rich information that they contain.
L3Harris Geospatial has continued its

The most popular spectral workflows are:> Atmospheric Correction> Change Detection

L3Harris Geospatial applied deep learning technology to airborne images captured over Nashville, Tennessee, following a deadly March 2020 tornado.
Using the ENVI Deep Learning module, which is offered as an extension to ENVI geospatial analysis software, technicians identified where building
damage had been inflicted by the tornado and charted its precise path of destruction from the aerial photos – in less than two hours.

TAPPING THE INTERNET OF THINGS (IOT)
Not all remote sensing devices fly on satellites and aircraft. Data-gathering sensors are all around us,
and efforts are being made to federate these ground observing sensors for the good of society.
MONITORING AND ANALYSISSERVICES BECOMING POPULAR

One of the areas where L3Harris has
made a valuable impact is in hyper-local
weather monitoring. Despite billions
spent on Doppler radar and satellite
assets, meteorologists still struggle togain insights into weather conditions
that occur on a hyper-local scale and inreal-time. Ground-based sensors candeliver complimentary information incontext not before considered especiallyto drivers on the highway and can save
lives. Fortunately, an extensive camera
sensor array capable of capturing local
conditions, already exists in the UnitedStates and across the world in the form ofexisting traffic and surveillance cameras.
L3Harris has tapped into a worldwide
set of 50,000 highway CCTVs to develop
a ground-level situational awarenesssystem called Helios®, which uses AI
technology to monitor the incoming video
streams from traffic cameras in real time.
Helios algorithms continuously analyze
the video from thousands of cameras
to detect specific conditions such as
precipitation, fog, wet/icy pavement
and stopped traffic. The algorithms
differentiate between snow, and rain
and their intensity.

“Fog kills more people than flooding in
Florida,” said Eric Dixon, Helios ProductManager at L3Harris Geospatial. “Thestate wants to know precisely when andwhere fog is occurring and heavy rains
can be a predictor of flooding, just as
snow can precede slick roads,”
said Dixon.
Helios, which is offered to state andfederal agencies, as well as privatecompanies, as a subscription service, cantrigger alerts when certain environmental
thresholds are exceeded. Localized
heavy rain information can be sent tometeorologists to adjust their forecasts,
and transportation agencies can better
manage slick roads or stopped traffic.
This information, in the context of adriver can be disseminated to the publicand directly to drivers in the affectedareas. Dangerous conditions detected by
Helios can even trigger warnings to bebroadcast on highway signs.
“Helios fills the gap in localized weather
information that could tell us what is
occurring locally during a big storm,
which is important information fordisaster managers to have,” said Dixon.

While the increase in data variety,volume and revisit frequency of remotesensing devices is a net positive for all
consumers of geospatial information, thenegative impacts are starting to be felt as
well. Specifically, the growing variety ofdata types and the sheer volume of themassive data sets is creating havoc forsome end users. Disaster management
professionals simply don’t have time tobecome subject matter experts on all
the different image data sets available
to them. And many organizations can’t
afford to buy and maintain the computing
infrastructure now required to process
massive remote sensing data files.
“The majority of our clients are buying
ENVI software and doing the work
themselves, but there is definitely apivot toward contracting for services,
especially related to ongoing monitoringof specific areas of interest,” said Griebel.
In response, L3Harris has rolled out
geospatial solutions services customized
to the needs of specific applications.
The client does not have to know which
type of imagery or analysis algorithms touse. All the client does is describe what
disaster management questions need to
be answered, and L3Harris takes care of
the rest – from choosing the right data
set and creating a practical analytical
workflow to providing the computerhorsepower and formatting the answer inan easy-to-understand format – tailoredfor disaster managers, first responders
and others.

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