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Capella Space Goes with AWS to Handle Satellite Downlinks

Earth observation data company takes a cloud native approach to reduce technology costs and be more responsive.

The challenges of capturing massive amounts of raw data from a satellite in orbit and converting it into usable products led Capella Space to turn to Amazon Web Services. Specifically, the AWS Ground Station service is being used for its satellite communications control and data processing resources. Capella Space plans to launch its first satellite this summer with more satellites to follow, says Scott Soenen, the company’s vice president of product engineering. “We have a goal to change the way that data is requested and accessed in Earth observation.”

Capella Space plans to make synthetic-aperture radar (SAR) data gathered by its satellites available for commerce and conservation efforts. SAR data is captured when satellites pass over the world’s topography, and it can be used to create detailed images and reconstructions. Soenen says he turns the raw data from satellites into geospatial, data products. A web platform is being built to make it easy to access such satellite imagery.

Soenen says AWS Ground Station services meet Capella Space’s expected needs, which could scale up quickly. Each satellite, Soenen says, may collect between 2TB and 5TB of raw data per day. Turning that data is turned into products increases the size significantly, he says. “The bit depth of the products is much higher than the raw data we downlink,” he says.

Capella Space launched in 2016 to become an Earth observation and information services company. Soenen says the company has been developing its spacecraft with commercial, off-the-shelf technology when possible to reduce costs compared with traditional satellite development. Capella Space evaluated various technology options, he says, such as building something itself or using other cloud providers but found AWS features and services to fit its needs. “That gives us the ability to be more agile and quicker in our development process,” Soenen says.

Traditional approaches to fulfilling Earth observation needs can take up to a month, he says, where customers call in orders that would be delivered on FTP. Capella Space wants to change that process with a completely automated system that is cloud native, Soenen says, leveraging new technology where possible. Users would submit tasking requests through a web application or an API and be able to query the catalog of satellite data, he says.

In order to put its strategy in motion, Soenen says Capella Space needed midlatitude ground stations to help the company lower data latency and be more responsive when requests come in. “The AWS Ground Station solution was compelling to us because they have plans for a large network of midlatitude stations,” he says. That can shorten the time between reception of requests, uplink to the spacecraft, and then downlink back to the ground station. “It’s all API driven, all automated, and we can build it into our cloud native approach,” Soenen says.

Earth observation can be used for defense and intelligence monitoring purposes, he says, where computer vision and machine learning techniques identify objects or targets within the data. It can also have ecological applications such as detecting illegal logging and deforestation. “The big advantage to using a SAR system is that we can do that observation at night or through clouds, which optical satellite imagery cannot do,” Soenen says. That can mean consistent monitoring, he says, with AWS enabling increased responsiveness.

After Capella Space gets its first satellite in orbit, Soenen says another launch is planned for later this year. More satellites are expected to be launched to meet demand anticipated from its customers. There will be a need to optimize data processing and workflow to ensure products are delivered quickly, he says. The ability of AWS to scale with high volumes of data will be central to those plans. “Responsiveness and low latency of delivery are key considerations for us,” Soenen says. “There’s a lot of steps from when we receive the raw data to turning it into useful images or geospatial data products.”

 

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