The original Earth observation stack

The Earth observation industry did not begin as a software industry. It began as an infrastructure problem.

For decades, the hardest part of observing Earth was not understanding the planet. It was simply getting access to imagery at all. Satellites were expensive, launches were rare, sensors were limited, downlink bandwidth was constrained, storage was costly, and processing pipelines were slow. Most organizations could not directly operate space systems, so the industry evolved around a single core principle: control access to pixels.

the load-bearing constraint. every business model, workflow, and tool downstream inherited it.

That principle shaped everything that followed. The original Earth observation stack was designed around acquisition. Every company, workflow, and business model ultimately revolved around the movement of imagery from orbit to human operators on the ground. The result was an ecosystem optimized for collecting data, cataloging data, and selling data — not understanding it.

In the early years of remote sensing, satellites were essentially scientific instruments operated by governments and defense organizations. Programs like NASA Landsat transformed humanity’s ability to observe the planet. For the first time, it became possible to repeatedly image Earth at planetary scale. But access was limited. Imagery was expensive to capture and difficult to distribute. Analysts often waited days or weeks to receive datasets. Data arrived in specialized formats that required domain expertise to interpret. Most workflows depended on dedicated geospatial software running on isolated workstations.

Earth observation was not a developer ecosystem. It was a specialist ecosystem. Remote sensing experts became the operating system of the industry — they understood spectral bands, atmospheric corrections, coordinate systems, radiometric calibration, and sensor artifacts. The average engineer could not simply build on top of EO infrastructure the way developers build on cloud infrastructure today.

The industry evolved accordingly. Instead of open platforms, EO became a chain of vertically integrated systems: satellite operators captured imagery, ground stations received it, processing systems corrected it, catalogs indexed it, analysts interpreted it, and governments and enterprises purchased reports derived from it. Every layer existed to support the movement of imagery through the pipeline.

As commercial Earth observation matured, competition centered around three fundamental metrics — resolution, revisit frequency, and coverage. These became the defining dimensions of the industry.

Higher resolution meant smaller objects could be detected. Companies competed to image roads, buildings, vehicles, ships, aircraft, and eventually individual pieces of infrastructure with increasing precision. Revisit frequency determined how often a location could be observed. A single image of a port had value; daily monitoring had exponentially more value; near real-time monitoring became the long-term ambition. Coverage defined how much of the planet could be captured at scale. Governments wanted continental visibility, agriculture required seasonal imaging across massive regions, and climate monitoring demanded persistent global archives.

The entire industry became a race to optimize these dimensions — better sensors, more satellites, larger constellations, faster downlinks, cheaper launches. The assumption was simple: if more imagery could be captured, more value would emerge. For a long time, that assumption held true.

the unspoken premise of an entire generation of EO companies. it broke quietly, then all at once.

As satellite imagery became commercialized, a new layer emerged above operators: imagery marketplaces. These platforms aggregated datasets from multiple providers and exposed searchable catalogs for customers. Instead of negotiating directly with individual satellite companies, users could discover and purchase imagery through centralized interfaces. The business model resembled stock photography marketplaces, except the assets were observations of the planet.

Customers searched by location, time range, cloud cover, resolution, and sensor type. If existing imagery was unavailable, users could submit tasking requests to satellites for future collection opportunities. This became one of the defining workflows of commercial EO — find imagery, purchase imagery, task imagery, download imagery, analyze imagery. The industry standardized around scenes, tiles, and archives.

Even when APIs emerged, they primarily existed to programmatically access imagery catalogs. Most APIs still fundamentally exposed pixels. Earth observation infrastructure became extremely sophisticated at moving images through systems. But very little infrastructure existed for understanding what those images meant.

Traditional remote sensing workflows were heavily manual. An analyst might spend hours or days searching imagery archives, evaluating cloud contamination, running preprocessing pipelines, performing atmospheric correction, calculating spectral indices, training classification models, and exporting shapefiles and reports. Every workflow required deep domain expertise.

Even simple questions became operationally complex.

The imagery itself rarely contained the answer directly. Humans had to derive the answer. This distinction became one of the most important limitations of the original EO stack: the system delivered observations, not intelligence.

It is easy to criticize the early Earth observation industry for being overly focused on imagery acquisition. But that focus was rational. Acquisition was the bottleneck. Before reusable rockets, cheap launches, cloud computing, and modern AI systems, simply collecting planetary-scale imagery was an extraordinary engineering challenge. The industry optimized around the scarcest resource: pixels.

every layer of the stack — storage, marketplaces, APIs, business models — inherited the same scarcity assumption.

Storage systems were built to archive pixels. Marketplaces were built to distribute pixels. APIs were built to retrieve pixels. Business models were built to monetize pixels. Very few systems were designed around semantic understanding.

In many ways, EO evolved similarly to the early internet. The first phase focused on connecting computers. The second phase focused on organizing information. Only later did platforms emerge that abstracted complexity and enabled entirely new applications. Earth observation is now entering that transition.

The original stack solved access to planetary imagery. The next stack must solve access to planetary understanding. That changes everything.