In a 2019 “Leaders” column, The Economist wrote, “A new age of space is beginning … Falling costs, new technologies, Chinese and Indian ambitions, and a new generation of entrepreneurs promise a bold era of space development.” Today, this sentiment has germinated ample space-tech startups in tech hubs from San Francisco, California to Shenzhen, China. The tyranny of space economics was such that the upfront costs would bankrupt a company before revenue could see daylight. But the pace of development in miniaturized hardware and robust software has changed the rules of the game.
The costs of launching, deploying, and observing satellites have dramatically fallen. A cube-sized satellite, dubbed “CubeSat,” costs anywhere from $50,000 to $200,000 to launch to low-Earth-orbit. If these numbers hold, we have entered a new era dominated not just by land, air, and sea, but also space. A commercialized space era might have just begun where the fundamentals of economics are pitted against the laws of physics.
In the civilian sector, satellites can broadly be categorized into two groups: communication based and sensor focused. Sensor-focused satellites are of particular interest given their scope of application, from reconnaissance to security to global observation. Maxar, a Colorado-based satellite imagery company, has been critical to military intelligence in the ongoing Russo-Ukrainian War, monitoring activities from the vantage point of low-mid-Earth orbit or geosynchronous geodesics.
Equally, in civilian matters, imagery satellites have stretched their posture. When the wildfire of 2019-20 (dubbed “black summer”) raged across the Australian territory of New South Wales, civilian reconnaissance played a decisive role in rescuing wildlife and preserving property. An estimated 12 million acres of forested land was met with scorching wildfire compounded by combustion-euphoric atmospheric conditions. Satellite-detected fire points pinned by data-driven algorithms became dexterous during the rescue missions. Increasingly, people’s interest has diverted towards the capabilities of geospatial intelligence in non-military fronts, and Silicon Valley has taken notice.
According to the Silicon Valley Business Report, “the space industry was a $380 billion business globally in 2020 and is expected to grow to $10 trillion by 2030,” and it “accounts for 0.5% of the global GDP.” Public-private partnerships involving space research and data sharing have only become emboldened by successful larger ventures like SpaceX’s rocket launches and the small startups contracting into their systems. The European Space Agency’s “Copernicus” program openly shares data from its Sentinel satellites with the public, with premium upgrades available at relatively low costs.
Imagery satellites have two major modes of data collection: real images that operate like your iPhone camera and SAR (Synthetic-Aperture Radar) images collected using radar. SAR is a form of cloud- and obstruction-piercing radar employed to generate two-dimensional images or three-dimensional reconstructions of objects, landscapes, or regions of interest.
While SAR takes images from satellites, LiDAR (Light Detection and Ranging), another preeminent optical mapping technology, is usually attached onto an aircraft or drone and flown at relatively lower altitudes for data collection. Known for delivering extremely high-resolution data, it equips analysts with detections of forest density or minor construction flaws. LiDAR has vertical accuracy of 10 centimeters (4 inches), and Maxar claims to deliver a native resolution of 30 centimeters with SAR.
If data is the new oil, then geospatial data is the jet fuel that’s commercialized and traded between companies. A complete picture generated with SAR and LiDAR is a goldmine to companies torturing themselves with modest sensor data, which lacks contextual awareness. With the falling costs of such geospatial data, it is slowly becoming an affordable yet powerful arsenal for companies in the transport, utility, maritime security, construction, and global development industries.
Even Wall Street traders are taking notice of this technology. Hedge funds are keen on beating Wall Street earnings forecasts and some have employed data scientists to predict sales based on the number of cars present in retailers’ parking lots. “The informational advantage yields 4% to 5% in the three days around quarterly earnings announcements, which is a significant return over such short window,” UC Berkeley professor Panos Patatoukas wrote in 2019.
While space programs were once a GDP-draining venture muscled by large economies, a growing number of midsize to smaller countries are at present capable of commissioning and maintaining their own space programs. This, in turn, stimulates their home-grown space-tech spinoffs—adept ones rightly outcompeting and faulty ones falling victim to bankruptcy. In a petri dish of proliferating startups, cut-throat competition for government contracts, pledges, and promises is inevitable.
A big headache in geospatial intelligence is finding signals in the colossal haystacks of noisy background. Petabytes of data lurking to forecast crop failures or predict cargo maritime security or global solar energy output is virtually a David-and-Goliath situation in signal processing. Remote sensing technologies and deep learning are dependably deployed to ingest this “big data” for characterization. Sentenel-1, for instance, does so with the enabled feature to acquire systematic datasets for monitoring vessels. Space technologies in earth observation and geospatial analysis have been going through their own renaissance since the major activities during the Apollo missions.
Regardless of the outcome, one thing is abundantly clear: space is up for sale.