Tag: hyperspectral imaging

Seekr launches beta for dual-use geospatial reasoning engine

Seekr has launched the beta testing of SeekrGeo, a geospatial reasoning engine. SeekrGeo provides advanced geospatial intelligence to enterprises and government agencies, accelerating actionable insights with launch partner Wyvern to deliver hyperspectral imaging capabilities.

Wyvern, a hyperspectral imaging and Earth observation data company, provides a comprehensive licensing agreement as Seekr’s inaugural data partner. The alliance accelerates enterprise access to scalable, high-resolution hyperspectral imaging powered by AI-driven analysis that can reason, detect changes over time, and identify meaningful patterns in activity for both national security and commercial use cases including wildland fire management, supply chain intelligence, and countless other actionable VLM-based insights.

As geospatial intelligence (GEOINT) grows to a projected $63B market by 2030, the gap between data availability and usable intelligence continues to widen. Bringing together Wyvern data and Seekr technology fills the gap in the market, giving enterprises and government customers a way to both access multimodal hyperspectral data, and synthesize intelligence and actionable insights with SeekrGeo’s Remote Sensing Foundation Model built for multimodal understanding, contextual reasoning, and autonomous analysis.

“Our first SeekrGeo customers required the use of Hyperspectral imaging to solve the most complex recognition problems. We recognized Wyvern for their best-in-class Hyperspectral LEO constellation and are very pleased to be working with them,” said Rob Clark, Seekr president.

“The biggest barrier to hyperspectral adoption has never been the data, it’s been the difficulty of turning that data into applications,” said Chris Robson, Co-Founder and CEO of Wyvern. “Seekr’s geospatial foundation model changes the equation entirely. Instead of needing months of specialized development work, our customers will be able to build new applications in a fraction of the time at scale.”

February 27, 2026
Drones detect moss beds and changes to Antarctica climate
GNSS and unmanned aerial vehicles (UAVs) have revolutionized precise mapping in polar regions. For a team from Queensland University of Technology (QUT), UAVs enabled a flexible platform for deploying hyperspectral imaging (HSI) sensors and collecting high-resolution data, enhanced by GNSS with real-time kinematic (RTK) to ensure accurate geolocation for reliable vegetation analysis.

The team turned to UAVs to meet the unique challenges of monitoring Antarctic vegetation. Harsh conditions, remoteness, limited access and climate variability make traditional field surveys time-consuming and costly. Worse, they risk disturbing sensitive vegetation, explain the researchers.

What Grows There. Antarctica’s terrestrial ecosystems are home to freeze-tolerant vegetation like mosses and lichens, which play a crucial role in biogeochemical cycles, soil insulation and supporting biodiversity. These organisms underpin the continent’s fragile ecosystems, increasingly threatened by climate change, extreme events, and human activitiees.

While satellite imagery enables large-scale observations, its limited spectral and spatial resolution, alongside cloud interference, constrains fine-scale vegetation analysis. HSI captures a broad wavelength range, enabling discrimination of vegetation by their spectral signatures. Multispectral imaging (MSI) data, such as that from Sentinel-2, is also being explored.

Each technology contributes uniquely:
- GNSS RTK provides georeferencing
- Machine-learning techniques enable precise segmentation
- UAVs offer flexible spatial coverage and high-resolution datasets.
However, unless these elements are integrated, mapping accuracy diminishes. Moreover, limited validation of spectral libraries and simulated imagery against field data restricts the reliability of remote sensing outcomes.

The team’s study addresses current gaps by building on the UAV-based HSI workflow that incorporates ground-based HSI data and MSI. “We expand this approach by integrating UAV-captured HSI data to enhance remote sensing capabilities in polar environments,” researchers explain. The updated methodology combines UAVs, high-resolution red, green, blue (RGB) imagery, and ground and aerial HSI data with machine-learning-based semantic segmentation.

The new workflow was evaluated in Antarctic specially protected area (ASPA) 135, Windmill Islands, East Antarctica, focusing on lichen detection and moss health mapping (Fig. 1).

Location of ASPA 135 (6616’60” S, 11032’60” E) and studied vegetation. (a) Map of Antarctica showing Casey Station’s location using the Polar Stereographic Projection. (b) Map delineating ASPA 135 (purple) near Casey Station (top left). (c) Ground-level imagery of moss and lichen at ASPA 135, along with surrounding rock and ice formations. (Credit: QUT)

Read the full study, “Drone hyperspectral imaging and artificial intelligence for monitoring moss and lichen in Antarctica,” on the Scientific Reports website.
July 28, 2025
Satellite space sensor to measure coastal and ocean ecosystems

Hyperspectral imagery of U.S. East Coast. (Image: NOAA)

Raytheon will build the Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) sensor under a contract from the University of New Hampshire. GLIMR, NASA’s selected Earth Venture Instrument-5 investigation, will be NASA’s first hyperspectral imager in geostationary (GEO) orbit.

Hyperspectral imaging collects and processes information from across the electromagnetic spectrum including visible light, infrared and ultraviolet frequencies to create a highly detailed view of physical and biological conditions in coastal waters.

The instrument will provide high-sensitivity, high-spatial and high-temporal resolution measurements of coastal and ocean ecosystems in the Gulf of Mexico, parts of the southeastern U.S. coastline and the Amazon River plume.

Decision-makers will use the GLIMR data to respond rapidly to natural and manmade coastal water disasters, such as harmful algae blooms and oil spills. It will also help improve the coastal ecosystem’s sustainability and resource management.

“GLIMR will collect the sharpest and most colorful view of physical and biological conditions in coastal waters ever seen from GEO,” said Jeff Puschell, GLIMR instrument scientist and principal engineering fellow at Raytheon Space Systems. “A hyperspectral imager is essential technology to capture new insight about our changing coastal ecosystems.”

The University of New Hampshire is NASA’s lead organization for the GLIMR contract. The instrument will launch aboard its host spacecraft in the 2026-2027 timeframe. Its data will be available to scientists, researchers and educators around the world.

August 26, 2019
Leica, Aibotix, and Headwall Offer Airborne Sensor Solution

The Aibotix X6.

Leica Geosystems, Aibotix and Headwall Photonics are offering an integrated high-performance airborne sensor solution using a hyperspectral imager and the Aibot X6 unmanned aerial vehicle (UAV). The Nano-Hyperspec sensor is optimized for size, weight and power to enable aerial acquisition of all spectral and spatial data within the scene of interest. A UAV with integrated Headwall sensor has been successfully flown and was presented at InterGeo 2014, held last week in Berlin.

Precision agriculture, forestry, geological research, and environmental monitoring are application areas that can benefit from the airborne hyperspectral imaging solution, the companies said. Equipped with the hyperspectral imager, the Aibot X6 can, for example, take pictures of fields or vineyards to determine the chlorophyll content, plant health, and invasive species, and offer farmers information on the state of the plants and harvest. By means of UAV and hyperspectral imager, farmers can measure before harvest, where the grain is driest or should be mowed first. Or, they can determine where rain has washed away fertilizer and where they must re-fertilize.

For geological mapping, landfills and open mine sites can be overflown to quickly and efficiently track precious metals or minerals. Environmental monitoring and research to derive contamination of soil or water can be determined spectrally from the air quickly using the Aibotix UAV and Nano-Hyperspec sensor.

The Nano-Hyperspec sensor measures 76.2 x 76.2 x 119.4 millimeters and weighs less than 0.68 kg. The sensor is integrated with a high-speed data processor and high-capacity flash storage. It collects image data across 640 spatial bands and 270 spectral bands with a Visible-Near-Infrared (VNIR) range of 400-1000 nm. The field of view is exceptionally wide, meaning that flight swath efficiency is maximized to cover as much territory as possible while the UAV is aloft. Further, it delivers crisp image data not only directly underneath the flight path but off to the edges.

The integrated data storage is 480 GB, which will yield more than two hours at a frame-rate collection rate of about 100 fps, which is matched to the actual performance of the UAV itself. The direct-attached GPS with IMU yields the ability to generate ortho-rectified imagery data products.

The Nano-Hyperspec comes pre-loaded with an airborne version of its Hyperspec III application software that manages sensor operation, image acquisition, and sensor performance while aloft. Hyperspec III software is designed to work in a complementary fashion with the GPS/IMU as well as incoming LiDAR data to collect spectral data and generate a completely integrated hyperspectral data cube.

October 13, 2014