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By NASA
4 min read
Entrepreneurs Challenge Winner PRISM is Using AI to Enable Insights from Geospatial Data
PRISM’s platform uses AI segmentation to identify and highlight residential structures in a neighborhood. NASA sponsored Entrepreneurs Challenge events in 2020, 2021, and 2023 to invite small business start-ups to showcase innovative ideas and technologies with the potential to advance the agency’s science goals. To potentially leverage external funding sources for the development of innovative technologies of interest to NASA, SMD involved the venture capital community in Entrepreneurs Challenge events. Challenge winners were awarded prize money, and in 2023 the total Entrepreneurs Challenge prize value was $1M. Numerous challenge winners have subsequently refined their products and/or received funding from NASA and external sources (e.g., other government agencies or the venture capital community) to further develop their technologies.
One 2023 Entrepreneurs Challenge winner, PRISM Intelligence (formerly known as Pegasus Intelligence and Space), is using artificial intelligence (AI) and other advances in computer vision to create a new platform that could provide geospatial insights to a broad community.
Every day, vast amounts of remote sensing data are collected through satellites, drones, and aerial imagery, but for most businesses and individuals, accessing and extracting meaningful insights from this data is nearly impossible.
The company’s product—Personal Real-time Insight from Spatial Maps, a.k.a. PRISM—is transforming geospatial data into an easy-to-navigate, queryable world. By leveraging 3D computer vision, geospatial analytics, and AI-driven insights, PRISM creates photorealistic, up-to-date digital environments that anyone can interact with. Users can simply log in and ask natural-language questions to instantly retrieve insights—no advanced Geographic Information System (GIS) expertise is required.
For example, a pool cleaner looking for business could use PRISM to search for all residential pools in a five-mile radius. A gardener could identify overgrown trees in a community. City officials could search for potholes in their jurisdiction to prioritize repairs, enhance public safety, and mitigate liability risks. This broad level of accessibility brings geospatial intelligence out of the hands of a few and into everyday decision making.
The core of PRISM’s platform uses radiance fields to convert raw 2D imagery into high-fidelity, dynamic 3D visualizations. These models are then enhanced with AI-powered segmentation, which autonomously identifies and labels objects in the environment—such as roads, vehicles, buildings, and natural features—allowing for seamless search and analysis. The integration of machine learning enables PRISM to refine its reconstructions continuously, improving precision with each dataset. This advanced processing ensures that the platform remains scalable, efficient, and adaptable to various data sources, making it possible to produce large-scale, real-time digital twins of the physical world.
The PRISM platform’s interface showcasing a 3D digital twin of California State Polytechnic University, Pomona, with AI-powered search and insights. “It’s great being able to push the state of the art in this relatively new domain of radiance fields, evolving it from research to applications that can impact common tasks. From large sets of images, PRISM creates detailed 3D captures that embed more information than the source pictures.” — Maximum Wilder-Smith, Chief Technology Officer, PRISM Intelligence
Currently the PRISM platform uses proprietary data gathered from aerial imagery over selected areas. PRISM then generates high-resolution digital twins of cities in select regions. The team is aiming to eventually expand the platform to use NASA Earth science data and commercial data, which will enable high-resolution data capture over larger areas, significantly increasing efficiency, coverage, and update frequency. PRISM aims to use the detailed multiband imagery that NASA provides and the high-frequency data that commercial companies provide to make geospatial intelligence more accessible by providing fast, reliable, and up-to-date insights that can be used across multiple industries.
What sets PRISM apart is its focus on usability. While traditional GIS platforms require specialized training to use, PRISM eliminates these barriers by allowing users to interact with geospatial data through a frictionless, conversational interface.
The impact of this technology could extend across multiple industries. Professionals in the insurance and appraisal industries have informed the company how the ability to generate precise, 3D assessments of properties could streamline risk evaluations, reduce costs, and improve accuracy—replacing outdated or manual site visits. Similarly, local governments have indicated they could potentially use PRISM to better manage infrastructure, track zoning compliance, and allocate resources based on real-time, high-resolution urban insights. Additionally, scientists could use the consistent updates and layers of three-dimensional data that PRISM can provide to better understand changes to ecosystems and vegetation.
As PRISM moves forward, the team’s focus remains on scaling its capabilities and expanding its applications. Currently, the team is working to enhance the technical performance of the platform while also adding data sources to enable coverage of more regions. Future iterations will further improve automation of data processing, increasing the speed and efficiency of real-time 3D reconstructions. The team’s goal is to expand access to geospatial insights, ensuring that anyone—from city planners to business owners—can make informed decisions using the best possible data.
PRISM Intelligence founders Zachary Gaines, Hugo Delgado, and Maximum Wilder-Smith in their California State Polytechnic University, Pomona lab, where the company was first formed. Share
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Last Updated Apr 21, 2025 Related Terms
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By NASA
Explore This Section Exoplanets Home Exoplanets Overview Exoplanets Facts Types of Exoplanets Stars What is the Universe Search for Life The Big Questions Are We Alone? Can We Find Life? The Habitable Zone Why We Search Target Star Catalog Discoveries Discoveries Dashboard How We Find and Characterize Missions People Exoplanet Catalog Immersive The Exoplaneteers Exoplanet Travel Bureau 5 Ways to Find a Planet Strange New Worlds Universe of Monsters Galaxy of Horrors News Stories Blog Resources Get Involved Glossary Eyes on Exoplanets Exoplanet Watch More Multimedia ExEP This artist’s concept pictures the planets orbiting Barnard’s Star, as seen from close to the surface of one of them. Image credit: International Gemini Observatory/NOIRLab/NSF/AURA/P. Marenfeld The Discovery
Four rocky planets much smaller than Earth orbit Barnard’s Star, the next closest to ours after the three-star Alpha Centauri system. Barnard’s is the nearest single star.
Key Facts
Barnard’s Star, six light-years away, is notorious among astronomers for a history of false planet detections. But with the help of high-precision technology, the latest discovery — a family of four — appears to be solidly confirmed. The tiny size of the planets is also remarkable: Capturing evidence of small worlds at great distance is a tall order, even using state-of-the-art instruments and observational techniques.
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Watching for wobbles in the light from a star is one of the leading methods for detecting exoplanets — planets orbiting other stars. This “radial velocity” technique tracks subtle shifts in the spectrum of starlight caused by the gravity of a planet pulling its star back and forth as the planet orbits. But tiny planets pose a major challenge: the smaller the planet, the smaller the pull. These four are each between about a fifth and a third as massive as Earth. Stars also are known to jitter and quake, creating background “noise” that potentially could swamp the comparatively quiet signals from smaller, orbiting worlds.
Astronomers measure the back-and-forth shifting of starlight in meters per second; in this case the radial velocity signals from all four planets amount to faint whispers — from 0.2 to 0.5 meters per second (a person walks at about 1 meter per second). But the noise from stellar activity is nearly 10 times larger at roughly 2 meters per second.
How to separate planet signals from stellar noise? The astronomers made detailed mathematical models of Barnard’s Star’s quakes and jitters, allowing them to recognize and remove those signals from the data collected from the star.
The new paper confirming the four tiny worlds — labeled b, c, d, and e — relies on data from MAROON-X, an “extreme precision” radial velocity instrument attached to the Gemini Telescope on the Maunakea mountaintop in Hawaii. It confirms the detection of the “b” planet, made with previous data from ESPRESSO, a radial velocity instrument attached to the Very Large Telescope in Chile. And the new work reveals three new sibling planets in the same system.
Fun Facts
These planets orbit their red-dwarf star much too closely to be habitable. The closest planet’s “year” lasts a little more than two days; for the farthest planet, it’s is just shy of seven days. That likely makes them too hot to support life. Yet their detection bodes well in the search for life beyond Earth. Scientists say small, rocky planets like ours are probably the best places to look for evidence of life as we know it. But so far they’ve been the most difficult to detect and characterize. High-precision radial velocity measurements, combined with more sharply focused techniques for extracting data, could open new windows into habitable, potentially life-bearing worlds.
Barnard’s star was discovered in 1916 by Edward Emerson Barnard, a pioneering astrophotographer.
The Discoverers
An international team of scientists led by Ritvik Basant of the University of Chicago published their paper on the discovery, “Four Sub-Earth Planets Orbiting Barnard’s Star from MAROON-X and ESPRESSO,” in the science journal, “The Astrophysical Journal Letters,” in March 2025. The planets were entered into the NASA Exoplanet Archive on March 13, 2025.
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Last Updated Apr 01, 2025 Related Terms
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By NASA
3 min read
2023 Entrepreneurs Challenge Winner Skyline Nav AI: Revolutionizing GPS-Independent Navigation with Computer Vision
NASA sponsored Entrepreneurs Challenge events in 2020, 2021, and 2023 to identify innovative ideas and technologies from small business start-ups with the potential to advance the agency’s science goals. To help leverage external funding sources for the development of innovative technologies of interest to NASA, SMD involved the venture capital community in Entrepreneurs Challenge events. Challenge winners were awarded prize money, and in 2023 the total Entrepreneurs Challenge prize value was $1M. Numerous challenge winners have subsequently received funding from both NASA and external sources (e.g., other government agencies or the venture capital community) to further develop their technologies.
Skyline Nav AI, a winner of the 2023 NASA Entrepreneurs Challenge, is pioneering GPS-independent navigation by leveraging cutting-edge computer vision models, artificial intelligence (AI), and edge computing.
Skyline Nav AI’s flagship technology offers precise, real-time geolocation without the need for GPS, Wi-Fi, or cellular networks. The system utilizes machine learning algorithms to analyze terrain and skyline features and match them with preloaded reference datasets, providing up to centimeter-level accuracy in GPS-denied environments. This capability could enable operations in areas where GPS signals are absent, blocked, degraded, spoofed, or jammed, including urban canyons, mountainous regions, and the Moon.
Skyline Nav AI’s flagship technology at work in New York to provide precise location by matching the detected skyline with a reference data set. The red line shows detection by Skyline Nav AI technology, the green line marks the true location in the reference satellite dataset, and the orange line represents the matched location (i.e., the location extracted from the satellite dataset using Skyline Nav AI algorithms). Skyline Nav’s visual navigation technology can deliver accuracy up to five meters, 95% of the time. The AI-powered visual positioning models continuously improve geolocation precision through pixel-level analysis and semantic segmentation of real-time images, offering high reliability without the need for GPS.
In addition to its visual-based AI, Skyline Nav AI’s software is optimized for edge computing, ensuring that all processing occurs locally on the user’s device. This design enables low-latency, real-time decision-making without constant satellite or cloud-based connectivity, making it ideal for disconnected environments such as combat zones or space missions.
Furthermore, Skyline Nav AI’s technology can be integrated with various sensors, including inertial measurement units (IMUs), lidar, and radar, to further enhance positioning accuracy. The combination of visual navigation and sensor fusion can enable centimeter-level accuracy, making the technology potentially useful for autonomous vehicles, drones, and robotics operating in environments where GPS is unreliable.
“Skyline Nav AI aims to provide the world with an accurate, resilient alternative to GPS,” says Kanwar Singh, CEO of Skyline Nav AI. “Our technology empowers users to navigate confidently in even the most challenging environments, and our recent recognition by NASA and other partners demonstrates the value of our innovative approach to autonomous navigation.”
Skyline Nav AI continues to expand its influence through partnerships with organizations such as NASA, the U.S. Department of Defense, and the commercial market. Recent collaborations include projects with MIT, Draper Labs, and AFRL (Air Force Research Laboratory), as well as winning the MOVE America 2024 Pitch competition and being a finalist in SXSW 2024.
Sponsoring Organization: The NASA Science Mission Directorate sponsored the Entrepreneurs Challenge events.
Project Leads: Kanwar Singh, Founder & CEO of Skyline Nav AI
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Last Updated Jan 07, 2025 Related Terms
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By Space Force
A prototype F-16 Fight Falcon cockpit collapsible ladder for agile combat employment and contingency operations emerged as the 2024 Spark Tank winner at the Pentagon.
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By NASA
The NASA Science Mission Directorate (SMD) instituted the Entrepreneurs Challenge to identify innovative ideas and technologies from small business start-ups with the potential to advance the agency’s science goals. Geolabe—a prize winner in the latest Entrepreneurs Challenge—has developed a way to use artificial intelligence to identify global methane emissions. Methane is a greenhouse gas that significantly contributes to global warming, and this promising new technology could provide data to help decision makers develop strategies to mitigate climate change.
SMD sponsored Entrepreneurs Challenge events in 2020, 2021, and 2023. Challenge winners were awarded prize money—in 2023 the total Entrepreneurs Challenge prize value was $1M. To help leverage external funding sources for the development of innovative technologies of interest to NASA, SMD involved the venture capital community in Entrepreneurs Challenge events. Numerous challenge winners have subsequently received funding from both NASA and external sources (e.g., other government agencies or the venture capital community) to further develop their technologies.
Each Entrepreneurs Challenge solicited submissions in specific focus areas such as mass spectrometry technology, quantum sensors, metamaterials-based sensor technologies, and more. The focus areas of the latest 2023 challenge included lunar surface payloads and climate science.
A recent Entrepreneurs Challenge success story involves 2023 challenge winner Geolabe—a startup founded by Dr. Claudia Hulbert and Dr. Bertrand Rouet-Leduc in 2020 in Los Alamos, New Mexico. The Geolabe team developed a method that uses artificial intelligence (AI) to automatically detect methane emissions on a global scale.
This image taken from a NASA visualization shows the complex patterns of methane emissions around the globe in 2018, based on data from satellites, inventories of human activities, and NASA global computer models. Credit: NASA’s Scientific Visualization Studio As global temperatures rise to record highs, the pressure to curb greenhouse gas emissions has intensified. Limiting methane emissions is particularly important since methane is the second largest contributor to global warming, and is estimated to account for approximately a third of global warming to date. Moreover, because methane stays in the atmosphere for a shorter amount of time compared to CO2, curbing methane emissions is widely considered to be one of the fastest ways to slow down the rate of global warming.
However, monitoring methane emissions and determining their quantities has been challenging due to the limitations of existing detection methods. Methane plumes are invisible and odorless, so they are typically detected with specialized equipment such as infrared cameras. The difficulty in finding these leaks from space is akin to finding a needle in a haystack. Leaks are distributed around the globe, and most of the methane plumes are relatively small, making them easy to miss in satellite data.
Multispectral satellite imagery has emerged as a viable methane detection tool in recent years, enabling routine measurements of methane plumes at a global scale every few days. However, with respect to methane, these measurements suffer from very poor signal to noise ratio, which has thus far allowed detection of only very large emissions (2-3 tons/hour) using manual methods.
This landscape of “mountains” and “valleys” speckled with glittering stars is actually the edge of a nearby, young, star-forming region called NGC 3324 in the Carina Nebula. Captured in infrared light by NASA’s new James Webb Space Telescope, this image reveals for the first time previously invisible areas of star birth. Credit: NASA, ESA, CSA, and STScI The Geolabe team has developed a deep learning architecture that automatically identifies methane signatures in existing open-source spectral satellite data and deconvolves the signal from the noise. This AI method enables automatic detection of methane leaks at 200kg/hour and above, which account for over 85% of the methane emissions in well-studied, large oil and gas basins. Information gained using this new technique could help inform efforts to mitigate methane emissions on Earth and automatically validate their effects. This Geolabe project was featured in Nature Communications on May 14, 2024.
SPONSORING ORGANIZATION
NASA Science Mission Directorate
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Last Updated Aug 20, 2024 Related Terms
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