EOSDA Reflects On Satellite Industry Trends For 2025
The satellite industry, a sector that has always been at the forefront of technological innovation, holds a critical role in solving some of the world’s most pressing challenges. From environmental monitoring to real-time communication, the advancements in this field have far-reaching implications. As we stand at the edge of 2025, it’s more important than ever to explore the breakthroughs expected to shape the future of satellite technology and its potential impact.
This blog post delves into the key trends anticipated for 2025, gathered from EOSDA expert opinions and in-depth analytics. While many of these trends are indeed fascinating, there is a twist: their true industry-changing effects might take longer to materialize than expected. Read on to discover why.
Getting an overview of upcoming breakthroughs in the satellite industry is always exciting. This field is steeped in technological advancements, and every year brings promises of innovation. To provide a fresh perspective, several EOSDA colleagues came together to discuss the trends identified by industry analysts for 2025. As they examined these predictions, one insight stood out: while these advancements are undeniably intriguing, most will not cause seismic shifts in the industry within the year.
Industry Development Trends: Small Satellites, AI, And GSaaS
The satellite industry’s development trends often highlight the growing capabilities of small satellites, the integration of artificial intelligence (AI), and the emergence of Ground Station as a Service (GSaaS).
Small satellites, in particular, are expected to proliferate, offering new possibilities for Earth observation. However, as we’ve been working with the small optical satellite EOS SAT-1 from its very launch, we know it is not enough to simply launch them into orbit.
The calibration and configuration required for these satellites to become operational can sometimes take up to a year. Additionally, if there are only a small number of such satellites in orbit, their revisit rates remain insufficient for substantial competition, making a satellite constellation necessary to achieve a good revisit frequency.
This means their immediate influence on satellite data analytics in 2025 might be minimal.
AI, on the other hand, continues to evolve and find its place in satellite data analytics. The EOSDA solutions already include AI models that are being used to classify crops and define field boundaries. This practical application already demonstrates the possibilities of AI to transform raw data into actionable insights. However, its scope remains somewhat limited within the industry.
The challenge with using AI lies in deriving meaningful conclusions from data tailored to niche applications. And there is still not enough data produced by satellites for that.
GSaaS is another trend often mentioned on the Internet. This innovative approach allows users to access ground station capabilities without investing in expensive infrastructure.
With GSaaS, providers like AWS enable users to pay only for the time they use the antenna, which is essential for downloading satellite data directly to their systems. The antenna acts as a vital link as it receives raw data transmitted from satellites and makes it accessible for further processing and analysis.
While this model promises accessibility, its primary audience includes data providers and research institutions rather than satellite analytics companies. The approach is tailored for organizations that handle raw satellite data acquisition and offers them a cost-effective alternative to setting up dedicated ground stations.
However, as a satellite data analytics provider, we anticipate that the GSaaS model will primarily serve as a cost-optimization tool, with limited potential to drive significant change in our industry.
R&D In Satellite Technology: Quantum Sensing, In-Orbit Services, And VHTS
Research and development in satellite technology continue to push boundaries.
The most exciting topic here is probably quantum sensing. It still sounds too futuristic, but it holds promise for improving signal-to-noise ratios in hyperspectral imaging. Let’s explain it in more detail.
Quantum sensing relies on the principles of quantum mechanics to achieve ultra-sensitive measurements of physical properties, such as magnetic fields, gravity, and even subtle environmental changes.
So, while conventional sensors detect macroscopic phenomena, such as temperature or pressure, quantum sensors exploit the behavior of individual particles, such as photons or atoms. This brings unprecedented accuracy and depth of data, or, in other words, vastly improves signal-to-noise ratios when it comes to remote sensing.
Now, when conventional satellite sensors operate, they capture data at specific wavelengths of light (or spectral bands) to analyze an object or scene. The number of spectral bands used is often limited by those in the visible or infrared range to capture a broad overview of Earth’s surface. The EOS SAT-1, for one, makes use of 11 spectral bands.
Hyperspectral imaging means capturing data across hundreds of contiguous spectral bands and deriving a much more detailed analysis of materials and their properties from it. Imagine detecting pest infestations or water pollutants before they become visible to the naked eye, or assessing flood or fire impacts with precise material differentiation.
However, conventional sensors often produce a significant amount of noise in hyperspectral imaging. This noise can stem from various sources, such as atmospheric interference, sensor limitations, or the sheer volume of data being captured. Conventional sensors may also struggle to differentiate between subtle spectral variations, leading to inaccuracies in the data.
Quantum sensors can mitigate these issues as they are expected to show a much better signal-to-noise ratio in data collection. They achieve this by utilizing quantum phenomena like superposition and entanglement, which allow for more precise measurements even in noisy environments.
Despite the potential, EOSDA colleagues agree that this technology is still years from finalization.
Quantum technologies are still experimental. We all heard about quantum computers, but sensors that could be installed in small satellites are still in the proof-of-concept stage. Their complexity and cost make them more of a long-term prospect than an immediate trend.
In-orbit services, such as satellite refueling and debris removal, are also among the promising developments for 2025. They are expected to extend satellites’ operational lifespans and address the growing issue of space debris.
Companies like Astroscale are already conducting trials to demonstrate these capabilities, such as approaching non-functional satellites and safely de-orbiting them to reduce the risk of collisions in space .
However, from the commercial standpoint, these services also remain in their infancy.
The regulatory hurdles and technical complexities involved in performing these operations mean it will likely take years before they become operational on a meaningful scale.
When mature, these technologies could significantly reduce the need to launch new satellites, offering a more sustainable and cost-effective approach to managing the ever-growing constellation of spacecraft in orbit.
Finally, Very High Throughput Satellites, designed to improve data transmission capabilities, are further along in their journey. They use multibeam antennas and adaptive modulation to efficiently deliver vast amounts of data, i.e. provide internet speeds that rival terrestrial broadband.
Notable examples of VHTS include satellites like Eutelsat KONNECT and ViaSat-3 , which are already operational and showcasing their potential to connect remote regions with high-speed internet access.
The advancement of VHTS also holds promising benefits for Earth observation as it would enable more rapid analysis and dissemination of critical environmental and observational data.
We’re looking forward to building and utilizing near-real-time applications in disaster response, agriculture, and climate monitoring once such satellites become available.
Software Development: Augmented Analytics, Real-Time Processing, And Data Fabric Architecture
Software continues to be a driving force in the satellite industry.
Augmented analytics, which combines AI with traditional data analysis, is already finding niche applications. To give an example, Oleksii Neskorovnyi mentioned the startups Saillog and PEAT, which enable farmers to upload photos of their crops to an app and receive immediate AI-generated analyses of potential crop diseases.
While augmented analytics holds promise in remote sensing, one of its key limitations is the availability of sufficient and timely data to train AI models effectively. The satellite industry generates a vast amount of data, but for AI-driven insights, we still lack enough fresh and comprehensive datasets. Furthermore, the limited availability of historical data also hampers the training of AI, as these systems rely on extensive datasets to identify patterns and make predictions.
Real-time data processing is another area of active development. While true real-time capabilities are not yet feasible, advancements in near-real-time processing are paving the way for faster insight delivery to the end user.
One unexpected way data processing can be accelerated is by reducing transmission times through onboard data compression and preliminary analysis technologies. By equipping satellites with computational units, raw data can be processed directly in orbit. This approach allows satellites to analyze collected data, extract key insights, and transmit only the most critical information back to Earth, significantly lowering bandwidth demands.
One practical example of this is the European Space Agency’s ɸ-Sat-1 .
It was launched back in 2020 and uses an AI chip onboard to filter out images obscured by clouds (among other tasks). This ensures that only useful data reaches ground stations.
As these computational units evolve, they could pave the way for fully autonomous satellite systems capable of performing complex analyses without ground intervention.
Finally, a relatively new concept of data fabric architecture offers a framework for automatic integration of diverse data sources — from satellite and drone imagery to ground-based sensors and even farmer verbal statements. Although still an emerging concept, it holds the promise of creating a unified analytical environment that will allow a more holistic understanding of complex phenomena. For instance, in Earth observations, combining satellite imagery with historical environmental data and sensor readings could help forecast and mitigate natural disasters more effectively.
However, as Oleksii Kryvobok pointed out, achieving seamless integration remains a challenge.
We are still grappling with technical barriers like data interoperability and the sheer volume of data involved, but overcoming these hurdles is essential for more effective decision-making.
A Future Is Full Of Possibilities
The satellite industry is brimming with potential, with trends like small satellites, AI, GSaaS, and hyperspectral imaging promising to redefine the sector. However, as the EOSDA team concluded, 2025 may not be the year of groundbreaking shifts. Many of these advancements require time, investment, and further development to reach their full potential.
Yet, the progress being made today lays the foundation for the breakthroughs of tomorrow. When those moments arrive, EOSDA is set to be at the forefront, ready to report, analyze, and adapt to the ever-evolving landscape of satellite technology.
About the author:
Maksym Sushchuk is at the forefront of realizing EOSDA's vision to make space tech a global driver of sustainability on Earth. He has over 15 years of experience in journalism and content creation for prominent Ukrainian startups, charitable funds and ESG businesses. As Head and Co-founder of PR Army Maxim brings attention to the human and social tolls of the aggression against Ukraine.
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