GISCafé Industry Predictions for 2023 – GeoSapient

BY John L. Kelley, President and Co-founder, GeoSapient, Inc.

John L. Kelley

GeoSapient looks beyond the growing space- and aerial-borne assets into the deeper aspects of geocomputing data, workflows, and use cases.

Theme: Hungry for Data

1. SmallSat Proliferation

SmallSats are proving to be a cornerstone of the Space Industry. Their low production costs and versatility enable more and more companies and groups to enter and exploit the potential offered by satellites. What are SmallSats? The size and cost of spacecraft vary depending on the application; some you can hold in your hand, while others, like Hubble, are as big as a school bus. Small spacecraft (SmallSats) focus on spacecraft with a mass of less than 180 kilograms and about the size of a large kitchen fridge. Even with small spacecraft, various sizes and masses can be differentiated. CubeSats are a class of nanosatellites (1-10 kilograms) that use a standard size and form factor. The standard CubeSat size uses a “one unit” or “1U” measuring 10x10x10 cms and is extendable to larger sizes; 1.5, 2, 3, 6, and even 12U.

The general proliferation of SmallSat companies building and launching their constellations for EO and radar will accelerate over the next several years. Mergers and acquisitions (M&A) will continue. There is virtually no limit to what SmallSats startups are focusing on: capabilities from in-space manufacturing, space situational awareness, IoT, space tourism, and more to span the globe.

A significant and growing concern is space debris. Remnants of old rockets, detritus from collisions, decommissioned satellites, and the proliferation of small satellites all contribute to an increasingly congested space domain. As orbits become increasingly congested, the concern of limited space and spectrum in low earth orbit is quickly becoming an issue that must be addressed. GeoSapient supports the U.S. announcement calling for an end to anti-satellite (ASAT) testing.

2. Remote Sensing Systems for Methane Detection

Satellite observations of atmospheric methane emissions are gaining attention due to their ability to quickly and frequently monitor large areas with global coverage. Uniquely positioned to provide near real-time information on rapid changes in emissions, satellites can help to improve global capabilities in quantifying country- or regional-level emissions, inform national methane reduction goals, and monitor emission patterns over time.

The two primary satellite-based methane detection and quantification types are area flux mappers and point source imagers. Area flux mappers cover broad regions using a large pixel size—anywhere from 100 meters to 10 kilometers— coupled with high-precision instruments to quantify methane emissions. Point source imagers use a finer-scale pixel size, with each pixel covering an area of fewer than 60 meters to focus on and quantify the plumes emitted from individual point sources.

Currently, at least 16 satellite systems that provide publicly accessible data and document methane-observing capabilities have been deployed or are slated for deployment in this decade.

Satellite observations of atmospheric methane in the shortwave infrared (SWIR) provide an increasingly powerful system for continuously monitoring emissions from the global scale down to point sources. As technologies, regulations, and operating practices continue to advance, finding solutions to challenges, such as connecting top-down methane emission information to improving bottom-up emission inventories, will become increasingly important.  This is a top-priority for industry operators and regulators, and presents a significant research development opportunity for entities that can develop scalable solutions to managing and integrating different types of detection and measurement data in a transparent and meaningful way.  Critical pathways that can combine different satellite-based instruments or pair satellite measurements with ground-based and airborne detection platforms will offer multi-tiered observing strategies that maximize detection abilities and the long-term effectiveness of satellite-based detection platforms.

3. Synthetic Data for AI Improvements

Synthetic data is vital because it can be generated to meet specific needs or conditions unavailable in existing (real) data. These data can be helpful in numerous cases, such as when privacy requirements limit data availability or how it can be used. What is Synthetic Data? Synthetic data generated from computer simulations or algorithms provides an inexpensive alternative to real-world data increasingly used to create accurate AI models. Put another way, synthetic data are created in digital worlds rather than collected from or measured in the real world. It may be artificial, but synthetic data reflects real-world data, mathematically or statistically. Research demonstrates it can be as good or even better for training an AI model than data based on actual objects, events or people.

Within a few years, remote sensing synthetic data will overshadow real data streamed from space. The drive is to build high-quality, high-value AI models that can only be done with the help of synthetic data for training, resiliency testing, and improvement of algorithms and model fidelity. Note that augmented and anonymized data are not typically considered synthetic data. However, it is possible to create synthetic data using these techniques. At GeoSapient, we continue investigating NVIDIA’s Omniverse with diverse remote-sensing data.

Also, a quick call out to the Rochester Institute of Technology (RIT) and the Digital Imaging and Remote Sensing Image Generation (DIRSIG™) solution, a physics-driven synthetic image generation model. The model can produce passive single-band, multi-spectral or hyper-spectral imagery from the visible through the thermal infrared region of the electromagnetic spectrum. The model also has a mature active laser (LIDAR) capability and an evolving active RF (RADAR) capability. Models can be used to test image system designs, create test imagery for evaluating image exploitation algorithms, and develop data for training image analysts.

4. Quantum Sensors & Algorithms for GHG Analytics

A quantum sensor uses a quantum system such as entangled atoms or particles of light (photons) to learn a parameter of the source—a magnetic field, for instance—to be measured. Quantum sensing exploits the properties of quantum mechanics to develop ultra-sensitive technology that can detect changes in electric and magnetic fields and motion.

GeoSapient expects increased investments and advancing research with quantum sensors and algorithms to identify and quantify greenhouse gas (GHG) emissions. With many governments and private sectors accelerating quantum technology research and development, quantum sensing applications will broaden and mature. Other quantum mechanics-based device explorations in computing, simulation and communications will have a profound impact on the growth of quantum sensing. A prime example to sense GHS with quantum systems would be the extension of Quantum Light Metrology (QLM) and Future Aerial. This collaboration continues to create groundbreaking technology using quantum sensors to detect methane gas to address methane leakage.

About Author:

John L. Kelley, President and Co-founder, GeoSapient, Inc.

Mr. Kelley is the President and Co-founder of GeoSapient, Inc. He has extensive experience in remote sensing systems engineering and associated geospatial applications. Before starting GeoSapient, Mr. Kelley spent most of his career in the aerospace industry developing remote sensing systems. Mr. Kelley’s passion is the subject of remote sensing of the Earth for environmental and geospatial applications. Driven by this passion, he co-founded GeoSapient to create a unique ‘Geospatial Knowledge’ platform not currently in existence. He also teaches Remote Sensing as an adjunct lecturer at Villanova University and has guest lectured on the subject.

John.Kelley@GeoSapient.com

Category: GIS Industry Predictios

This entry was posted on Monday, January 23rd, 2023 at 9:56 am. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.