Planetary Systems AI Awarded by U.S. Space Systems Command’s Space Domain Awareness (SDA) TAP Lab An Annual License Subscription

From the period of 6 August through 29 October 2024, Planetary Systems AI successfully demonstrated the ability to use generative AI to read large quantities of semi/unstructured text and imagery to populate the Lab’s Target Model Database (TMDB). 

New York, NY, November 19, 2024Planetary Systems AI (PSAI), a planetary support company providing cyber-first artificial intelligence and machine-learning solutions for space and satellite operations, announced today that it has been awarded an annual subscription license by U.S. Space Systems Command’s Space Domain Awareness (SDA) Tools Applications and Processing (TAP) Lab after the completion of the Apollo Accelerator Cohort 4. This program enabled PSAI to demonstrate its capabilities with the use of generative AI to read large quantities of semi/unstructured text and imagery to populate the Lab’s Target Model Database (TMDB). The TMDB, once populated with details about a satellite’s payloads, power, and propulsion systems can be used to evaluate potentially threatening close approaches.

“With the amount of orbital traffic and payloads being deployed into space, it is imperative that a continuous monitoring and coverage of space assets traffic and anomaly management occurs 24/7/365.” said CEO & Chief Space Officer Cindy Chin. “PSAI is leveraging our multi-modal AI expertise and capabilities to work with the U.S. government, its allies, and our commercial partners to ensure that their decision support is accelerated and enhanced through our AI solutions. Our team was excited to showcase these tools and capabilities during the SDA TAP Lab Cohort 4 Demo Day with U.S. Space Systems Command, the U.S. Space Force, DARPA, and other government and industry partners.” 

SDA TAP Lab Chief, Major Sean Allen says, “This is a real innovation applying modern software to age-old problems and a great use-case for generative AI.” 

During the three-month TAP Lab cycle in Cohort 5, PSAI will further test and refine its solutions for SDA by increasing space vehicle imagery to its AI model, responding with a team in a given scenario related to threat warning and assessment. The current database is structured for direct integration with, query by, and display in SDA tools. It can be used with maneuver-event data for inferring potential for threats and determining proximity. Entries were filtered for validity by an AI model trained on Joint Commercial Operations (JCO) Notice to Space Operators records and other trusted analytic sources.

About SDA TAP Lab (https://sdataplab.org/): The Space Domain Awareness TAP Lab accelerates the delivery of space battle management software to operational units. We decompose kill chains, prioritize needs with operators, map needs to technologies, and onboard tech to existing platforms quickly. We partner with industry, academia, and across the government to succeed. 

About Planetary Systems AI (www.planetarysystems.ai): PSAI is a planetary support company accelerating data flow and insight generation for decision-making in the space sector, optimizing planetary support operations.

 

Planetary Systems AI Press Contact:
Mack Reed
Head of Product
E: pr@planetarysystems.ai

Download a PDF of this Press Release

 

Second in a series of notes from our residency at SDA TAP Lab:

We are working to solve a core problem in business:  Everyone is drowning in data and starved for insight. 

No-one feels this pain more deeply than people in the booming domain of space operations.

Rockets blast off from U.S. spaceports that still operate on antiquated, stovepiped 20th-century hardware and software not designed for the digital age – 232 launches are scheduled this year (given no mission delays).

Satellites orbit the earth with data packets crossing space in myriad formats, languages, and even purposes among their users, operators and stakeholders.

So rather than flowing, data drips sluggishly through channels gated by piecemeal infrastructure and security and intellectual-property protocols – or choked by the need to translate it from one use case to the next on a case-by-case basis.

Here in Colorado Springs, we are collaborating with other companies to answer a uniquely complicated data-flow question: Is that space debris or a satellite threat? 

The challenge here is that intelligence data flows in many forms from many sources towards the U.S. Space Force Space Systems Command, which is responsible for safety and national security. There, human operators must filter the real threats out of more than 44,000 other satellites and rocket bodies, and hundreds of thousands of particles of debris orbiting Earth.

So our SDA TAP Lab teams are collaborating on methods of sorting through all that data to help SSC operators decide whether to flag an object: Threat? Non-Threat? Or simply Unknown?

At PSAI, we are seeking to understand how the operators make those decisions today so they can use data from new “events” more effectively.

We believe that by making these data sources interoperable – and understanding the meaning that those decisions give to the data, we can help them take action in the future with with greater clarity, confidence, and speed.

Watch this space.

#spacedata, #sdataplab, #decisionsupport, #ai, #artificialintelligence, #ussf, #satellites, #satellitedefense

New York, NY, July 30, 2024 – Planetary Systems AI (PSAI), a planetary support company providing cyber-first artificial intelligence and machine-learning solutions for space and satellite operations, announced today that it has been selected to participate in the U.S. Space Systems Command’s Space Domain Awareness (SDA) Tools Applications and Processing (TAP) Lab Apollo Accelerator Cohort 4. This program enables PSAI to demonstrate its machine learning models and AI product solutions for decision support in satellite operations and space domain awareness. 

“With the amount of orbital traffic and payloads being deployed into space in the expansion of the space economy, it is imperative that a continuous monitoring and coverage of space assets traffic and anomaly management occurs 24/7/365.” said CEO & Chief Space Officer Cindy Chin. “PSAI is leveraging our AI and machine learning roots and capabilities to work with our government and commercial customers and partners to ensure that their decision support is accelerated and enhanced through our AI solutions, even through multi-classification systems. Our team is excited to showcase these tools and capabilities during the SDA TAP Lab with U.S. Space Systems Command, U.S. Space Force and other government and industry partners.” 

During the three-month TAP Lab cycle, PSAI will further test and refine its solutions for SDA by responding with a team in a given scenario related to threat warning and assessment. This includes the company’s solutions for ontology SDK, multi-classification architecture, data normalization and processing of semi-structured, unstructured, and synthetic test data, as well as its management dashboards.

About SDA TAP Lab (https://sdataplab.org/): The Space Domain Awareness TAP Lab accelerates the delivery of space battle management software to operational units. We decompose kill chains, prioritize needs with operators, map needs to technologies, and onboard tech to existing platforms quickly. We partner with industry, academia, and across the government to succeed. 

About Planetary Systems AI (www.planetarysystems.ai): Planetary Systems AI (PSAI) is a planetary support company developing decision support systems for operational efficiency, situational awareness, and logistical planning to serve companies, government agencies, and small businesses that will increasingly rely on clarity and speed in multiple- context data sources that they must consult to make decisions around space and satellite operations. 

Planetary Systems AI Press Contact:

Mack Reed, Head of Product

E: pr@planetarysystems.ai


Download a PDF of this press release

#ssa #ssc #sdataplab #dod #psai #planetarysystemsai #ai #spaceai #machinelearning