BigBear.ai’s fourth quarter revenue increase year over year by 20.6% from $33.5 million to $40.4 million. The company’s revenues for 2022 increased to $155 million, which was a $9.4 million increase from $145.6 million in 2021.
Patent filed for new lithium mineral location technology
TEL AVIV, Israel (ASTERRA PR) — Today, ASTERRA announced a patent filing on advancements in using PolSAR-based technology for lithium exploration that will greatly accelerate the identification of lithium (LI) deposits. The patent was based on extensive field testing for validation.
“The expansion to mining is a natural progression of our ability to use AI analytics to monitor soil moisture underground,” said Elly Perets, CEO of ASTERRA. “It also fulfills our mission to become humanity’s eyes to protect the environment.”
ASTERRA’s complex artificial intelligence (AI) and machine learning (ML) algorithms extract the signal of lithium concentration underground from satellite based PolSAR data and can pinpoint locations containing high lithium. This technology creates a way to find lithium before investing in costly exploration with intensive labor, and where it may result in environmental destruction and civil conflicts.
Neuraspace’s advanced space debris monitoring and satellite collision avoidance system is already being tested by some the biggest satellite operators.
The product is aimed at Satellite Operators, Insurers, Regulators and Policy Makers.
COIMBRA, Portugal (Neuraspace PR) — Neuraspace, a Portuguese start-up which is developing an advanced system for monitoring and preventing collisions in space, has announced the market availability of its product starting today.
NASA is funding a trio of research and development (R&D) projects by Nanoracks, Teltrium Solutions and Emergent Space Technologies aimed at enabling swarms of small satellites to better operate in Earth orbit and to explore other worlds.
The companies each received Small Business Innovation Research (SBIR) Phase II awards worth $750,000 to continue work on the their technologies. They each received smaller awards under the first phase of of the program.
Nanoracks, which is based in Houston, is focused on reusing spent rocket stages known as Outposts to help improve communications with satellite swarms exploring the moon and other planets.
The effort will enable US customers to use the DIRSIG™ model with Rendered.ai’s cloud capabilities to generate Earth Observation datasets for AI training
BELLEVUE, Wash. and ROCHESTER, N.Y. (Rendered.ai PR) — Rendered.ai, the leading platform for physics-based synthetic data, and the Rochester Institute of Technology’s Digital Imaging and Remote Sensing (DIRS) Laboratory today announced a collaboration to combine the physics-driven accuracy of the DIRSIG synthetic imagery model with Rendered.ai’s cloud-based platform for high volume synthetic data generation.
Machine Learning (ML) algorithms using Computer Vision (CV) data provide a key tool for exploiting the rapidly expanding capability and content of Earth Observations (EO) collection and analytics companies around the world. Rendered.ai provides a platform as a service (PaaS) for data scientists and CV engineers to scalably produce large, configurable synthetic CV datasets in the cloud for training Artificial Intelligence (AI) and ML systems.
The DIRSIG model produces a range of simulated output representing passive single-band, multi-spectral, or hyper-spectral imagery from the visible through the thermal infrared region of the electromagnetic spectrum. DIRSIG is widely used to test algorithms and to train analysts on simulated standard imagery products. The Rendered.ai team has built simulators for visible light and synthetic aperture radar (SAR), however DIRSIG’s breadth of capability and ongoing investment by granting agencies will provide qualified Rendered.ai customers a much wider range of field-tested and production-quality sensor modeling technology.
“DIRSIG has been providing synthetic imagery to expert customers for decades,” said Scott Brown, Ph.D., principal scientist and project lead. “Our collaboration with Rendered.ai enables us to bring our proven capability to a wider audience at a time when satellite and other forms of remote sensing data collection are rapidly expanding.”
Space debris is a major threat to the satellite services we rely on
13 projects involve industry and academia across the UK
SWINDON, UK (UK Space Agency PR) — The UK Space Agency is providing £1.7 million [US $2.3 million] for new projects to support sustainable space operations, Science Minister George Freeman announced today.
The 13 new projects will help track and remove dangerous debris in space. They include an AI-based tool which can take autonomous action to avoid a collision and another which will see multiple small spacecraft fired at debris before taking it into the atmosphere to dispose of it.
MOFFETT FIELD, Calif. (NASA PR) — Scientists recently added a whopping 301 newly validated exoplanets to the total exoplanet tally. The throng of planets is the latest to join the 4,569 already validated planets orbiting a multitude of distant stars. How did scientists discover such a huge number of planets, seemingly all at once? The answer lies with a new deep neural network called ExoMiner.
COLUMBIA, Md. (BigBear.ai PR) – BigBear.ai, a leading provider of artificial intelligence, machine learning, cloud-based big data analytics, and cyber engineering solutions, announced today that it has entered into a landmark software agreement with Terran Orbital, a global leader and pioneer in the development, innovation and operation of small satellites and earth observation solutions. BigBear.ai’s AI-powered insights will enhance Terran Orbital’s manufacturing, operations, and multi-source Earth imaging offering for U.S. government, international defense and commercial clients.
The commercial agreement represents a long-term partnership between BigBear.ai and Terran Orbital. Together, BigBear.ai and Terran Orbital plan to pioneer AI solutions with the use of new data collected from Terran Orbital’s planned NextGen Earth Observation constellation and BigBear.ai’s existing data analytics platform. Terran Orbital and BigBear.ai are working to revolutionize AI using insights gleaned from the New Space ecosystem.
SPRINGFIELD, Va. (NGA PR) — The National Geospatial-Intelligence Agency awarded a new flexible contract that is designed to enable best of class analytic solutions for economic related GEOINT challenges.
The five-year operational contract awarded at $29 million will compete delivery orders across the five selected vendors: BAE, Ball Aerospace, BlackSky, Continental Mapping Consultants, and Royce Geospatial Consultants Inc. These vendors were competitively selected among a diverse pool of companies.
“Understanding economic activity and trends around the world is critically important to our policymakers,” said Dave Gauthier, Director, Source Commercial Business and Operations Group at NGA. “I am excited to see innovative commercial solutions and geospatial analytic services that may provide new insights into the flows of raw materials, agricultural products, equipment, fuels, vehicles, waste products and other goods.”
WESTMINSTER, Colo. (Maxar Technologies PR) — Maxar Technologies (NYSE:MAXR) (TSX:MAXR), a trusted partner and innovator in Earth Intelligence and Space Infrastructure, today announced it has been awarded a five-year contract worth up to $26.4 million by the U.S. National Geospatial-Intelligence Agency (NGA) to sustain and enhance the National System for Geospatial Intelligence Open Mapping Enclave (NOME). Under the agreement, Maxar will continue to provide engineering, software development and geospatial tradecraft in support of NOME.
The web-based NOME platform enables a community of vetted U.S. government users to create and update geospatial features in a crowdsourced “living map.” NOME was initially developed to support NGA’s foundation mapping mission, but the platform emerged as a powerful tool to perform unclassified mission support remotely throughout the COVID-19 pandemic. Increasingly, users also leverage the platform to create features for further dissemination across multiple networks and to train artificial intelligence and machine learning (AI/ML) models.