How can Satellite Data be used for Competitive Intelligence?

Satellites, Data, and Competitive Intelligence

The concept of competitive intelligence is as old as business itself. Knowing what a rival is doing, how a market is shifting, or where a supply chain is strained has always provided a decisive edge. For decades, this intelligence was gathered on the ground: through market reports, supplier gossip, financial analysis, or site visits. Today, a new, powerful, and objective source of information operates hundreds of miles above the planet: commercial satellites.

What was once the exclusive domain of spy agencies and national governments has rapidly become a vital tool for the private sector. A new generation of private companies has launched thousands of sophisticated sensors into orbit, creating a torrent of data about activity on Earth. This data, when analyzed, provides a real-time, unbiased ledger of global economic activity.

This article explores the world of satellite-based commercial intelligence. It examines the technologies involved, the data they produce, and how industries – from automotive and energy to retail and insurance – are using this “view from above” to make high-stakes decisions. It’s a story of how photons, radar pulses, and radio waves are translated into actionable business strategy, revealing the physical movements of the global economy in unprecedented detail.

The Tools of Orbital Observation

Understanding satellite CI begins with understanding the different types of sensors orbiting the Earth. It’s not just one type of “camera.” Instead, a fusion of different data types is used to build a complete picture. Each sensor type has unique strengths and weaknesses, and their data is often layered together for a more complete analysis.

Electro-Optical (EO) Imagery

The most intuitive form of satellite data is electro-optical (EO) imagery. These are extremely powerful digital cameras in space. They capture sunlight reflecting off the Earth’s surface, creating the detailed “satellite photos” most people are familiar with.

• What it’s good for: EO provides high-resolution, familiar, and easy-to-interpret images. You can see cars in a parking lot, ships at a dock, construction progress on a new factory, or the exact number of shipping containers in a port.
• Limitations: EO sensors are passive; they rely on the sun. This means they can’t see at night. Their biggest vulnerability is weather. A single cloud can hide a target, making consistent monitoring difficult in many parts of the world.
• Key Players: Companies like Maxar Technologies are known for their very high-resolution imagery (able to resolve objects smaller than half a meter). Planet Labs operates a massive constellation of smaller “Dove” satellites, which provides a lower-resolution but history-making capability: imaging the entire landmass of Earth, every single day. Airbus Defence and Space is another major provider of high-resolution optical data.

Synthetic Aperture Radar (SAR)

Synthetic Aperture Radar (SAR) is perhaps the most powerful and versatile tool in the commercial CI toolbox. Unlike EO sensors, SAR is an active sensor. It doesn’t take pictures; it builds them.

A SAR satellite fires a beam of radar (microwave) pulses at the ground and then records the “echo” that bounces back. By analyzing the timing, intensity, and texture of these return signals, it can create a detailed image of the surface.

• What it’s good for: SAR’s primary advantages are its all-weather, day-and-night capabilities. Radar pulses slice directly through clouds, fog, smoke, and darkness. This makes it ideal for monitoring notoriously cloudy regions or tracking activity 24/7. SAR is exceptionally good at detecting man-made objects (like ships and vehicles, which reflect radar strongly), assessing ground moisture (for agriculture), measuring oil slicks on water, and detecting millimeter-level changes in ground height (like subsidence at a mine).
• Limitations: SAR images are not intuitive. They look like grayscale, grainy photos to the untrained eye and require significant expertise to interpret. A “bright” spot on SAR isn’t necessarily bright in visible light; it’s just highly reflective to radar (like metal).
• Key Players: A wave of “NewSpace” companies has revolutionized this field. ICEYE in Finland, Capella Space, and Umbra Lab in the U.S. have launched constellations of small, powerful SAR satellites, making high-revisit radar data commercially available at scale.

Multispectral and Hyperspectral Imaging

These sensors capture light from outside the visible spectrum. Multispectral imaging typically captures data from a few specific bands (like visible red, green, blue, plus near-infrared). Hyperspectral imaging captures data from hundreds or even thousands of very narrow bands.

• What it’s good for: This technology provides a “chemical fingerprint” of what it’s looking at. The primary commercial use is in agriculture. By analyzing how much near-infrared light a crop reflects, analysts can determine plant health, water stress, and nutrient deficiencies with high accuracy, long before the human eye could spot a problem. This is used to forecast crop yields. It’s also used in mining for mineral prospecting and in environmental monitoring to identify pollutants.
• Limitations: Like EO, these sensors are often passive and struggle with clouds. The sheer volume of data from hyperspectral sensors also presents a significant analytical challenge.

Radio Frequency (RF) Monitoring

RF monitoring satellites don’t take pictures at all. They “listen.” Their sensors are designed to detect and geolocate a wide rangeof radio frequency emissions from Earth.

• What it’s good for: This includes tracking signals from the Automatic Identification System (AIS) on ships, Automatic Dependent Surveillance–Broadcast (ADS-B) from aircraft, and even GPS jamming, communications signals, or the activity of maritime radar systems. For CI, this allows for the tracking of ships or planes even when they are out of range of ground-based receivers. It can also identify ships that have “gone dark” by turning off their AIS transponders, a common practice in illicit activities like sanctions evasion or illegal fishing.
• Key Players: Companies like HawkEye 360 and Kleos Space operate satellite clusters that fly in formation. By using multiple satellites to triangulate a single signal, they can pinpoint its location on Earth with remarkable precision.

The Data Pipeline: From Photons to Profit

A raw satellite image is just a collection of pixels. A raw RF signal is just noise. The value isn’t in the data itself, but in the answers extracted from it. This transformation is a multi-step process handled by a sophisticated “downstream” analytics industry.

The Problem of Big Data

Modern satellite constellations generate petabytes (thousands of terabytes) of data. Planet’s constellation alone images the entire Earth daily. A single high-resolution SAR image can be gigabytes in size. No team of human analysts could possibly sift through this data deluge manually.

It’s impossible to have an analyst stare at daily images of every Walmart parking lot or every oil port in the Middle East. The scaling problem required a new solution.

The Solution: AI and Machine Learning

The breakthrough that unlocked satellite CI for mainstream business was the application of Artificial Intelligence (AI) and Machine Learning (ML). Analytics companies train algorithms to perform specific tasks at a massive scale.

• Object Detection: Computer vision models are trained to automatically find and count objects. They can scan images of a port and return a precise count: “14 cargo ships, 2 oil tankers, 3,405 shipping containers.” They can scan a Tesla factory lot and count every single car, often even sorting them by model or color.
• Change Detection: Algorithms compare a new image to a “baseline” image of the same location. They automatically flag only what has changed. This is used to spot new construction, deforestation, or the appearance of heavy machinery at a mining site.
• Pattern Recognition: ML models can identify complex patterns that hint at larger trends. They can learn the “normal” level of activity at a factory (trucks in/out, thermal signature from smokestacks) and automatically alert a user when that activity spikes or plummets, suggesting a change in production.

The Power of Data Fusion

The most advanced form of satellite intelligence comes from data fusion – the practice of layering multiple data types together. A single data source can be misleading. Combining them creates a verifiable, high-confidence picture.

Imagine an analyst trying to track a specific shipment of crude oil.

1. RF Monitoring detects an AIS signal from a tanker leaving a port in Saudi Arabia.
2. The ship then “goes dark,” turning off its AIS beacon as it nears a sanctioned country.
3. An analyst tasks a SAR satellite to scan the area where the ship disappeared. The SAR image finds a large, metal vessel matching the tanker’s description, even though it’s cloudy.
4. An EO satellite (on a clear day) is then tasked to get a high-resolution visual confirmation, capturing the ship’s name or unique deck features.
5. This fused data package provides a complete, verifiable track of the vessel, which would be impossible with any single sensor.

Case Study: The Automotive Industry

The automotive industry is a complex, global web of suppliers, manufacturers, and distributors. Satellites provide visibility into every link of this chain.

Monitoring Manufacturing and Production

The most classic satellite CI use case in the auto sector is “car counting.” Hedge funds and market analysts purchase data from companies that use AI to automatically count the number of finished vehicles in the storage lots of major auto plants.

By tracking this inventory on a daily or weekly basis, an analyst can derive a factory’s production rate. If inventory at a Volkswagen plant in Germany is rising steadily, it may suggest that production is outpacing demand (a bearish signal). If inventory at a General Motors plant is suddenly empty, it can signal a successful quarter (a bullish signal) or a production halt due to a supply chain problem (a bearish signal). This data often provides a more accurate, real-time measure of activity than the company’s own quarterly reports.

This monitoring extends beyond finished cars. Analysts also track:

• Employee Parking Lots: The number of cars in an employee lot is a direct proxy for a factory’s shift activity. A sudden drop in cars can indicate a shutdown or a strike.
• Factory Expansion: Satellite imagery can spot a competitor’s new factory or plant expansion months or even years before an official press release. Analysts can monitor construction progress, estimate the project’s timeline, and gauge the scale of the investment.
• Thermal Monitoring: Multispectral sensors can detect heat. By monitoring the thermal output from a factory’s paint shop or foundry, analysts can gauge activity levels even at night or inside buildings.

Supply Chain and Raw Material Intelligence

Cars aren’t built in one place. They rely on a global flow of raw materials like lithium, cobalt, rubber, and steel, as well as thousands of individual parts.

• Tracking Key Components: Using a combination of AIS, RF, and EO/SAR imagery, analysts track the container ships carrying essential parts (like batteries or microchips) from Asia to factories in Europe or North America. Port congestion, visible from space, can be factored into models predicting production delays.
• Raw Material Sourcing: Satellite data is used to monitor activity at key commodity sites. An analyst can watch the expansion of a lithium brine pond in Chile, measure the size of iron ore stockpiles at a mine in Australia, or track logging activity at a rubber plantation. A disruption at one of these key nodes can have ripple effects across the entire auto industry.

Case Study: Oil and Gas

The oil and gas industry was one of the earliest adopters of commercial satellite intelligence. For a commodity driven purely by supply and demand, any advance information on supply is worth billions.

Upstream: Exploration and Production

Before a single drill breaks ground, satellite imagery (especially multispectral) is used in exploration to identify geological formations and surface features that suggest a high probability of oil or gas reserves.

Once production starts, satellites are used for monitoring:

• Rig Counting: Instead of relying on official reports, analysts can use SAR or EO imagery to count the number of active drilling rigs in regions like the Permian Basin in Texas. This is a fundamental indicator of investment and future production.
• Gas Flaring: Multispectral sensors can detect the heat and light from natural gas flaring (the burning of excess gas at a wellhead). The intensity and number of flares provide a direct measure of oil and gas extraction activity.
• Environmental Monitoring: SAR is extremely effective at detecting oil slicks on water. This is used to monitor for pipeline leaks or spills from offshore platforms, allowing companies like ExxonMobil or Shell to manage risk and respond to incidents faster.

Midstream: Storage and Transportation

This is where satellite data has its most direct and famous impact on financial markets.

The “floating roof tank” is the standard container for storing crude oil at “tank farms” around the world. As the name implies, its roof floats on top of the oil. As the tank fills, the roof rises; as it empties, the roof falls.

SAR satellites provide the key. A SAR sensor, scanning at an angle, hits the rim of the tank. If the roof is low (empty tank), the radar pulse has to travel further to hit the floating roof, and the “shadow” it casts in the radar image is large. If the roof is high (full tank), the shadow is tiny.

By precisely measuring the size of this shadow, data analysts can calculate the height of the roof and, by extension, the exact volume of oil in the tank. By doing this for every major tank farm on Earth every few days, companies like Kayrros can create an independent, real-time estimate of global crude oil inventories.

Before this, traders had to wait for weekly, often-inaccurate government reports. Now, they can see supply levels changing in near real-time, giving them an enormous edge in trading oil futures.

The same principles apply to transportation. Using a combination of AIS, RF, and SAR/EO, analysts track the global tanker fleet. They don’t just see where ships are; they see how low they sit in the water (a “laden” ship is full, an “unladen” ship is empty), confirming which ships are actually carrying oil and which are not.

Case Study: Commodities and Natural Resources

The “big picture” view from space is perfectly suited for monitoring agriculture and natural resources, which are spread over vast, often remote areas.

Agriculture and Food Security

Hedge funds and major agricultural conglomerates like Bayer (which owns The Climate Corporation) and Deere & Company are major consumers of satellite data.

• Yield Forecasting: Using multispectral data, analysts assess crop health across entire continents. By comparing the “greenness” (a measure known as NDVI) of this year’s corn crop in Iowa to the same period last year, they can build highly accurate models that predict the season’s total yield. This information is available weeks before official government (USDA) projections, allowing traders to buy or sell futures contracts accordingly.
• Drought and Damage: When a drought hits Brazil‘s coffee-growing region, satellite data (both EO and SAR, which measures soil moisture) can quantify the exact area and severity of the impact.
• Supply Chain Monitoring: Analysts track activity at grain silos, sugar refineries, and processing plants. They can see how large the stockpiles are outside a facility or how many trucks are in the queue, indicating the pace of the harvest.

Mining and Metals

For metals like iron ore, copper, and coal, satellites are used to track supply from the mine to the final customer.

• Stockpile Measurement: At ports and mines, these commodities are kept in massive, open-air piles. Satellite imagery, sometimes combined with SAR or even stereo EO images (using two images to create a 3D model), is used to calculate the precise volume of these stockpiles. A growing pile at a port might signal weak demand from buyers like China.
• Activity Monitoring: Analysts track the number of trucks, trains, and ships at major mines and ports. They can spot the expansion of an open-pit mine or identify new tailing ponds.
• Illegal Mining: Satellite data, particularly high-revisit SAR, is a primary tool for governments and NGOs to identify and track illegal gold mining operations in environmentally sensitive areas like the Amazon rainforest.

Expanding Horizons: Other Sectors

The applications for satellite CI are spreading to nearly every corner of the economy.

Retail and Commercial Real Estate

What started with agriculture and oil has moved to Main Street.

• Parking Lot Analysis: This is the retail equivalent of “car counting.” Data firms ingest daily satellite images of thousands of retail locations for companies like Home Depot, Target, or Best Buy. AI algorithms count the cars in each parking lot. This foot-traffic data is aggregated and sold to hedge funds as a direct indicator of a retailer’s in-store sales. It provides a powerful, independent data point for predicting quarterly earnings.
• Construction Monitoring: Real estate developers and investors use satellite imagery to monitor construction progress on new malls, distribution centers, and residential communities. This helps verify that projects are on schedule. It’s also used for competitive scouting, identifying where rivals are building new locations.
• Distribution Center Monitoring: The flow of trucks in and out of an Amazon or Walmart distribution center is a direct proxy for the movement of goods and the health of the e-commerce sector.

Insurance and Disaster Response

The insurance and reinsurance industry uses satellite data for both underwriting and claims.

• Risk Underwriting: Before issuing a policy, an insurer can use satellite data to assess risk. For wildfirerisk, they can analyze the proximity of vegetation to a specific property. For flood risk, they can use historical data to map a property’s true flood exposure, not just relying on official maps.
• Damage Assessment: This is a major application. After a hurricane, flood, or wildfire, it’s often impossible to send human adjusters into the disaster zone. All-weather SAR satellites can image the area immediately, even through smoke or clouds, to identify the hardest-hit neighborhoods. High-resolution EO imagery follows, allowing insurers to perform a “triage” of claims, sometimes even approving payments for totally destroyed homes based on satellite imagery alone, all within days of the event.

Maritime and Global Supply Chain

The COVID-19 pandemic exposed the fragility of the global supply chain. Satellite monitoring became a key tool for understanding the bottlenecks.

• Port Congestion: Analysts use AIS and satellite imagery to count the number of container ship vessels waiting offshore at key chokepoints like the Port of Los Angeles or the Port of Shanghai. This “queue” of ships is a direct indicator of supply chain stress and future inflation.
• “Dark Vessel” Tracking: As mentioned, the fusion of RF, SAR, and EO data allows for the tracking of ships engaging in illicit activities. This is used by governments to enforce sanctions, by fisheries to stop illegal “dark fleet” fishing, and by maritime insurers to monitor vessels in high-risk areas.

The Business of Orbital Insight

A new ecosystem of companies has formed to service this demand. They are broadly split into three categories.

1. Upstream (The Operators): These are the companies that design, build, launch, and operate the satellite constellations. This is a capital-intensive business of “metal in space.” This category includes Planet Labs, Maxar Technologies, Airbus Defence and Space, ICEYE, Capella Space, and HawkEye 360.
2. Midstream (The Data Platforms): These companies act as aggregators. They sign deals with the upstream operators to pull in massive amounts of raw data. They clean it, archive it, and build APIs and platforms that make it easy for analysts to search and acquire data from multiple sensors in one place.
3. Downstream (The Analysts/Solutions Providers): This is where the “answers” are generated. These firms rarely own satellites. They buy data from the upstream and midstream, feed it into their proprietary AI/ML models, and sell information products to end-users. Kayrros (energy), Ursa Space Systems (general economic indicators), and RSMetrics (retail and real estate) are all examples. Their product isn’t “a satellite image”; it’s “a weekly report on Chinese crude oil inventories” or “a daily feed of foot traffic to 500 retail chains.”

The end customers are equally diverse. The earliest adopters were hedge fund managers and commodity traders seeking an information edge. Today, the customer base has expanded to include corporate strategy departments, risk managers, and supply chain logisticians at Fortune 500 companies.

Limitations, Ethics, and the Future

Satellite-based competitive intelligence is not a crystal ball. It has significant limitations and raises complex questions.

Technical and Analytical Limitations

• Resolution: Commercial satellites cannot read a license plate or identify a person’s face. The highest-resolution commercial imagery is around 30 centimeters, meaning it can distinguish an object of that size.
• Context is King: Data without context is misleading. An empty employee parking lot could mean a factory is shut down, or it could be a public holiday. A large stockpile of coal could mean low demand, or it could be strategic stockpiling in anticipation of high demand. Satellite data must be combined with other intelligence sources.
• The “Last Mile” Problem: A satellite can’t see inside a building. It can see trucks arriving at a factory and thermal data suggesting the factory is running, but it can’t see the specific assembly line or know the factory’s output efficiency.

Ethical and Privacy Concerns

The idea of commercial companies imaging any point on Earth creates undeniable privacy concerns. While the focus is on commercial and industrial sites, the capability to monitor public spaces or even private property exists. The law is still catching up to the technology. In most jurisdictions, observing commercial, public-facing areas (like a factory lot) from a public vantage point (which space is considered) is legal. However, the ethics of persistent surveillance, even of non-personal assets, are a subject of ongoing debate.

The Future of Orbital Intelligence

The industry is moving toward “real-time” monitoring.

• Growing Constellations: The number of satellites, particularly SAR and RF, is exploding. This shrinks the “revisit rate” – the time between observations of the same spot. The goal of many constellation (satellite) operators is to be able to image any point on Earth every hour, or even more frequently.
• On-Orbit Processing: The new bottleneck is getting data from the satellite to the ground and analyzed. The next generation of satellites is being designed with powerful AI chips onboard. The satellite will be able to analyze the image as it’s taken, identify a “change of interest” (like a ship entering a port), and send down just that small packet of information, rather than the massive raw image file.
• New Sensor Types: Companies are developing other sensors, such as satellites that can detect methaneemissions from space (a major concern for climate change and the energy industry) or even space-based video, which will capture motion and activity in short bursts.
• Democratization: As launch costs fall and the number of satellites rises, the cost of this data is declining. While still expensive, this information is becoming accessible to smaller companies, NGOs, and academic researchers, leveling the playing field and creating new applications, from environmental protection to human rights monitoring.

Summary

Satellite-based commercial intelligence has fundamentally altered how businesses understand the physical world. It provides an objective, large-scale, and timely source of truth on economic activity, independent of government reports or corporate press releases.

By translating data from optical, radar, and radio frequency sensors into measurable insights, this technology allows companies to monitor their supply chains, anticipate market shifts, and understand their competitors’ physical operations in detail. What began as a niche tool for spies and hedge funds is now an integrated part of the global business landscape. As the constellations grow and the AI becomes more powerful, the planet’s economic ledger is being written, revised, and read in near real-time from orbit.