Earth Observation (EO) is undergoing a revolution. Satellite hardware is cheaper, launch costs have plummeted and computing power and environmental resiliency continue to improve. Where legacy EO satellites once cost hundreds of millions of dollars, modern smallsats and cubesats can now be built and launched for one to two orders of magnitude less, offering broad or mission-specific capabilities at a fraction of the cost — albeit with tradeoffs in resolution, on-board power and satellite lifespan. Yet despite these advancements, broad access to EO data remains surprisingly constrained.
Growing the level of understanding of today’s space asset capabilities for EO on a broader scale, as well as exposing the next generation of technologies and solutions being fielded will be key to expanding EO visibility. This, coupled with opening and simplifying access to EO information will drive engagement across sectors, resulting in an overall “convenience” improvement. Increasing the number of coincident and/or networked sensors, and processing at the space-sensor-edge are two additional contributors towards addressing the “performance” and “price” barriers slowing EO utility adoption today. Only then can current use-cases expand and improve, and new use-cases be born.
The opportunity
EO data already plays a critical role in defense, climate monitoring, agriculture, energy, insurance, logistics and disaster response. Governments and enterprises increasingly rely on satellite-derived insights to drive decisions.
However, the downstream EO market — focused on data access, processing and integration — has not evolved at the pace of the space and ground hardware and systems utilized. Historically collected data may be abundant and the ability to gather tasked data has improved, but for many users, it remains difficult and costly to access and apply.
Five core barriers to EO adoption
High cost of access and analysis: While some EO data is technically available at low or no cost — such as from government satellites like Landsat or Sentinel — the expense lies in turning that data into something usable. Downloading, processing, storing and analyzing imagery requires significant infrastructure and expertise. Commercial data sources often come at a significant expense and generally require data minimums and data sizes that often exceed the use-case areas of interest. Small datasets can be captured, but often involve additional costs to access and utilize the associated ground-based infrastructure once the larger datasets are already collected and downlinked. For many potential users, especially in the private sector, these combined burdens can make EO-based solutions prohibitively expensive.
Data standardization remains a work in progress: EO data varies widely by provider — different spatial resolutions, spectral bands, formats and metadata conventions. This fragmentation makes it difficult to create scalable tools, automate analysis or integrate multiple data sources. Developers must often build custom pipelines for each source, creating additional pain points that limit broader use and scalability.
Closed ecosystems and limited interoperability: While some satellite operators and EO providers are working towards more open, flexible systems, many still use proprietary platforms and APIs, limited export options or “walled garden” ecosystems. Users are often locked into the provider’s toolchain, which can stifle innovation and integration of third-party tools and complicate connectivity with other datasets, systems or analytics platforms.
Restrictive data ownership models: EO data is typically controlled by the satellite operator or vendor that collects it. Licensing is often unclear, restrictive or incompatible with wide-scale commercial reuse and/or requires additional licensing fees. This inhibits downstream applications, discourages or limits the ability to fuse data across providers and limits overall market growth.
Connection between capabilities and sector expertise: Launch prices have lowered barriers for space companies to reach low Earth orbit, while technologies and components for space systems have advanced and continue to become increasingly available in parallel. This has helped foster the proliferation of space assets for EO use, however the business models and use-cases for satellite-based EO have largely been conceptualized by the space organizations bringing the physical capabilities to bear rather than a more diverse group. Users and analytics professionals within the wide range of applicable industry sectors have had limited exposure and access to these relatively new EO and AI/ML capabilities — slowing the ideation and solution implementation pipelines.
Having been heavily invested in the many facets of hardware and software design, development, and production of aerospace systems and solutions throughout the last 25 plus years of my career, I’ve realized just how easy we can collectively remain confined to our own echo chamber while not seeing enough of the macro perspective. Once a component, sub-system, or fully integrated space system is created, launched, or operating on-orbit, it can be easy to assume other individuals, groups, agencies, or companies will know how to use it, what to do with it, and why. Focusing on improvements that impact “performance”, “reliability”, and “cost/price” of the hardware and software within each element of the value chain provides much-needed gains.
However, just like the smartphone, acceleration and broad adoption was enabled by application stores. Centralized warehouses of applications; simple, one-click discovery, download, and install; and turn-key hosting drove rapid proliferation and scale of use-cases through “convenience.” Incremental and step-change or disruptive solutions to address each of these four core bases of competition are needed.
Fortunately, a number of innovative approaches are already emerging to address these challenges — offering more scalable, interoperable and cost-effective EO solutions.
Emerging Solutions and Shifting Paradigms
On-orbit data processing: Instead of downlinking large volumes of raw imagery, satellites equipped with onboard processing can identify, crop or analyze data at the source, transmitting only what’s most relevant. This significantly reduces bandwidth demands, accelerates delivery and reduces cost. More importantly, it reflects what many end users actually need: timely, purpose-cropped imagery, or direct insights derived from onboard analytics. By bringing compute to the data collection sensors in space, the path from collection to actionable intelligence and ultimately decision-making is significantly shortened. With the increased focus on improving the size, weight, power, and processing performance of on-board computers for space environments over recent years, achieving these gains can now be realized in the near very near term.
Multi-sensor constellations: Modern EO constellations are increasingly designed with a mix of complementary sensors — such as optical, hyperspectral, and thermal. By capturing coincident or near-simultaneous observations across different modalities, these systems deliver richer, more contextualized insights. This multi-sensor approach enhances detection accuracy, enables cross-validation and supports a broader range of real-world applications from energy to precision agriculture from a smaller suite of in-space hardware and operations infrastructure. Although some satellite systems and constellations provide coincident sensing, proliferation, revisit rates, and commercial access is limited, which makes pricing high and slows adoption. Aside from the performance benefits and increased sensing breadth, offering multi-sensor satellites as part of a single integrated constellation greatly reduces the cost of fielding, operating, improving and maintaining a system that spans a wide range of industry sectors and use cases.
Federated marketplaces and open access platforms: Platforms that aggregate imagery from multiple providers and offer flexible, API-based access allow users to browse, clip and download only what they need. These marketplaces lower costs, simplify discovery and foster price competition. They also simplify the ability to connect multiple data sources or vendors as and when needed. Continued efforts to embed analytics in both ground-based and in-orbit platforms to remove friction and make data and associated processed results less “technical” and more accessible as well.
Flexible licensing and shared ownership models: Usage-based pricing, time-bound access rights and pooled ownership are new approaches to addressing EO information access, adoption and proliferation, while providing opportunities for less restrictive ownership models. Shared and open-access constellations offer a solution that can minimize the overall space-based infrastructure costs while enabling broad sensing capabilities and simple adaptation of new advancements in technologies across sensing and processing solutions. These models also provide users more clarity and control while allowing more efficient monetization of derived applications, insights and intelligence across customer tiers. This shift in licensing helps address friction around cost, access rights and downstream reusability.
Further public and private support areas
Dramatic advancements in satellite costs, sensing and computing are already in work and being implemented within the value chain. As a result, EO data and use continues to gain momentum. However, a significant opportunity for scale remains. As discussed above, solving these challenges requires a shift in both business models, as well as products and architectures. Additionally, unlocking broad adoption and utilization of EO can be further enabled by:
- Governments, public agencies, policy makers, and funders investing and incentivizing a broader shared data, insights and intelligence infrastructure; particularly where national security, climate or disaster response are involved.
- Industry consortia driving towards standardization to support deep aggregation, as well as integration and more widespread adoption of continued advancements in analytics/AI/ML. Aspects of this are already in motion, but increasing commercial company engagement across the value chain as well as industry vertical analytics experts and end-users will continue to improve alignment.
- All stakeholders in the EO value chain participating in the infrastructure and usage simplification. Just as the smartphone and marketplaces did for the “app revolution,” and in similar fashion, the broad and rapid adoption of “conversational AI,” access and utility must be intuitive and user friendly. Implementing some of the suggested solutions above such as more “open access” are a first step here, followed by more accessible marketplaces and more attractive business models for developers. This will increase user adoption, understanding and trust – facilitating long-term engagement, more frequent utilization and rapid new use-case innovation.
Reducing friction is yet another key to making satellite intelligence a core asset across sectors.
Implications across sectors
As barriers to access fall, a consistent pattern emerges: reducing the cost of EO data acquisition, processing and integration, when combined with simplified accessibility and broad understanding of “the possible” is what ultimately drives broader adoption. When actionable insights become more affordable, EO shifts from a specialized tool to a core operational asset. Examples include but are most certainly not limited to:
- Government: Lower-cost access to timely, targeted insights enables faster, more effective decision-making in defense, disaster response, infrastructure monitoring and environmental compliance.
- Agriculture: Cost-effective, high-resolution imagery and analytics allow even smallholders to adopt precision agriculture techniques once reserved for industrial-scale operations, while furthering use-cases and adoption across the entire sector.
- Finance and Insurance: Affordable access to timely monitoring improves underwriting, asset tracking and risk assessment — unlocking EO adoption at scale.
- Energy: Lower data and processing costs make it viable to monitor widespread assets — such as pipelines, transmission lines, solar and wind farms — with greater frequency and fidelity.
- Research and NGOs: The reduced cost of historical and real-time imagery empowers research institutions and nonprofits to conduct longitudinal studies, track environmental changes and continue enabling evidence-based policy.
Together, these efforts can make EO data not just more available — but more useful, acknowledged by the masses and publicly impactful.
Scott Steffan is co-founder and Chief Revenue Officer of NOVI Space, a space AI infrastructure and compute company, where he helps drive NOVI’s revenue growth and dual-use edge‑processing constellation strategy. He has over 25 years of experience in aerospace, including launching Moog’s New Space Group and leading a large portfolio of Moog’s Space Exploration business.
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