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Aerial Products

Putting lives back together is your team’s mission. Supporting your team is ours.

Faster Claims Processing. Better Customer Service

Some CAT events allow for planning and preparation, others happen without warning. In both scenarios, there’s often chaos on the ground. Public access can be restricted for days.

Your customers are displaced. They need to know what has happened, and what will happen next. And, it is your team’s job to gather data and insights to act as swiftly as possible.

Our Property Claims Command Center’s CAT Response product is specifically designed to provide the fastest possible aerial insights to enable your claims teams to make critical decisions and process claims faster than ever before.

Curved Monitor with screen SWIPE

Fast access to detailed aerial data drives critical decision making


Our Team Becomes. YOUR Team.

Aerial team

When a CAT Event is brewing or when something unexpected happens, your Airbus team is there planning and tasking to ensure the most up-to-date imagery is available to you. Our in-house teams become an extension of your team, adding unparalleled expertise and infrastructure.

Pre-Event Planning

Some CAT events allow for planning and preparation, others happen without warning. In both scenarios, there’s often chaos on the ground. Public access can be restricted for days.

During the Event Tasking

Your situational awareness is powered by our ability to expedite tasking of the Airbus-owned constellation of satellites in your area(s) of interest.

After the Event Tasking

Let our machine learning help prioritize your team’s efforts and outreach by recommending PIFs likely damaged.

Increasing Claims Processing Volume

Using Aerial imagery of an impacted area allows claims teams to make accurate, high-volume decisions to plan and deploy resources in a fraction of the time it would take to manually inspect properties.

The Airbus-owned constellation of satellites provides up-to-date pre-event premium imagery and combines it with the fastest post-event imagery in the industry.

Overlaying your PIF data and analyzing the impact of an event is simple and easy. Our AI and machine learning algorithms help your teams prioritize claims handling based on impact, saving precious time. Getting enough information about the level of damage to determine the first step and which type of adjuster should get the claim, efficiently enables staffing of CAT events with the right personnel.



Your team can see the impact of an event before a customer can even file a claim

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Access-Restricted Area
Insights Within 6 Hours

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Total-Loss Claims Handled
From Desktops

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Easy Tasking Gathers More
Details When Needed

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Export Report For Claims File

Get Access To All Your Imagery Needs In One Place

Our constellation of Airbus-owned satellites and trusted network of manned and drone image acquisition options ensures you quickly get the images you need to coordinate your CAT response planning.

Harvey-1 Rockport Sat 50cm


Good for wide areas, fastest to access

1.5 m resolution globally, 50 cm resolution in cities

Harvey-3 Rockport Manned 29cm Closer


Higher resolution for wide areas

7.5-15 cm resolution

Harvey-4 Rockport Drone 3cm Closer


Highest resolution for assessing individual properties

Sub-5 cm resolution


PIF Example

Categorize PIF damage with AI & Machine Learning Processes

When a CAT event occurs, the sheer magnitude of Claims to be processed can instantly overwhelm even the most well-tuned CAT Response claims teams. Using machine learning to categorize and prioritize damage to PIFs in your AOI significantly increases your team’s claims processing potential.


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An analytic layer that uses machine learning to identify properties and determine whether or not a given property has been damaged. This is a binary evaluation, only providing outputs of “Likely Impacted” or “Unlikely Impacted”. It does not distinguish between varying levels of damage (e.g. “Total Loss” vs “Partial Loss”). Each property evaluation is associated with a confidence interval, which can be represented as a percentage of likelihood that the given evaluation is correct. For example, an evaluation of “Likely Impacted” with a confidence interval of .9788 means that the machine learning model is 97.88% confident that the “Likely Impacted” output is correct.

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A layer that highlights all identifiable water within a given image. This is not change detection, as in it simply displays all water visible on the image and does not discern between expected water (lake, pool, etc) and unexpected water (flood, rain, etc).

Thumb flood finder


A layer that compares the presence of water between two images and highlights all the water that was not present in the first image. By highlighting all water that was not there before (AKA not where it’s supposed to be), you effectively get a map of all the flood water in a given image.

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A layer that attempts to identify piles of rubble within an image for purposes of identifying total losses.

Thumb smoke mask


A layer that highlights all obstructing smoke within a given image. This layer is used to identify areas on the image that are covered in smoke so that our Fusion Analytics can tell users which of their assets are obscured by smoke and are therefore not viewable in the image.

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A process that attempts to cut out critical areas within smoky/hazy imagery and enhance the cutout region for clearer viewing. The result is a sharper, less hazy image that can often make the difference between useless and actionable imagery.

A Trusted Partner for Aerial Data

Part of trusted international aerospace pioneer Airbus, Aerial is delivering the future of geospatial services –today. Our mission is to enable customers to solve problems by providing access to many forms of aerial imagery along with analytics and tools to turn imagery into action.