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All of the World’s Spaceports on One Map



World map showing spaceports and missile test sites

Mapped: The World’s Rocket Launch Sites

From Sputnik 1 to today’s massive satellite constellations, every object in space was launched from just a handful of locations.

The map above, from BryceTech, is a comprehensive look at the world’s spaceports (both orbital and sub-orbital) as well as ballistic missile test sites.

ℹ️ In sub-orbital spaceflight, a spacecraft reaches outer space, but it doesn’t complete an orbital revolution or reach escape velocity. In orbital spaceflight, a spacecraft remains in space for at least one orbit.

The World’s Major Spaceports

Though the graphic above is a detailed list of many types of rocket launch sites, we’ll focus on major sites that are sending satellites and passengers into sub-orbit, orbit, and beyond.

Launch FacilityLocationCountry
Cape Canaveral Space Force StationFlorida🇺🇸 U.S.
Cape Canaveral SpaceportFlorida🇺🇸 U.S.
Kennedy Space CenterFlorida🇺🇸 U.S.
Cecil Field SpaceportFlorida🇺🇸 U.S.
Colorado Air & Space PortColorado🇺🇸 U.S.
Vandenberg Air Force BaseCalifornia🇺🇸 U.S.
Mojave Air and Space PortCalifornia🇺🇸 U.S.
Oklahoma Air & Space PortOklahoma🇺🇸 U.S.
Poker Flat Research RangeAlaska🇺🇸 U.S.
Pacific Spaceport ComplexAlaska🇺🇸 U.S.
Spaceport AmericaNew Mexico🇺🇸 U.S.
Launch Site One (Corn Ranch)Texas🇺🇸 U.S.
Houston SpaceportTexas🇺🇸 U.S.
Midland Air & Space PortTexas🇺🇸 U.S.
SpaceX Development and Test FacilityTexas🇺🇸 U.S.
SpaceX StarbaseTexas🇺🇸 U.S.
Spaceport CamdenGeorgia🇺🇸 U.S.
Mid-Atlantic Regional SpaceportVirginia🇺🇸 U.S.
Wallops Flight FacilityVirginia🇺🇸 U.S.
Reagan Test SiteKwajalein Atoll🇲🇭 Marshall Islands
Naro Space CenterOuter Naro Island🇰🇷 South Korea
Sohae Satellite Launching StationNorth Pyongan Province🇰🇵 North Korea
Kapustin YarAstrakhan Oblast🇷🇺 Russia
Plesetsk CosmodromeArkhangelsk Oblast🇷🇺 Russia
Vostochny CosmodromeAmur Oblast🇷🇺 Russia
Yasny Launch BaseOrenburg Oblast🇷🇺 Russia
Arnhem Space CentreNorthern Territory🇦🇺 Australia
Whalers Way Orbital Launch ComplexSouth Australia🇦🇺 Australia
Koonibba Test RangeSouth Australia🇦🇺 Australia
Bowen Orbital Spaceport Queensland 🇦🇺 Australia
Rocket Lab Launch Complex 1Wairoa District🇳🇿 New Zealand
Baikonur CosmodromeBaikonur🇰🇿 Kazakhstan
Space Port OitaŌita🇯🇵 Japan
Tanegashima Space CenterKagoshima🇯🇵 Japan
Uchinoura Space CenterKagoshima🇯🇵 Japan
Taiki Aerospace Research FieldHokkaido🇯🇵 Japan
Hokkaido SpaceportHokkaido🇯🇵 Japan
Ryori Launch SiteIwate🇯🇵 Japan
Sonmiani Satellite Launch CenterBalochistan🇵🇰 Pakistan
Integrated Test RangeOdisha🇮🇳 India
Thumba Equatorial Rocket Launching StationKerala🇮🇳 India
Satish Dhawan Space CentreSriharikota🇮🇳 India
Guiana Space CentreKourou🇬🇫 French Guiana
Barreira do Inferno Launch CenterRio Grande do Norte🇧🇷 Brazil
Alcântara Space CenterMaranhão🇧🇷 Brazil
Stasiun Peluncuran RoketWest Java🇮🇩 Indonesia
Jiuquan Satellite Launch CenterGansu Province🇨🇳 China
Taiyuan Satellite Launch CenterShanxi Province🇨🇳 China
Wenchang Spacecraft Launch SiteHainan Province🇨🇳 China
Xichang Satellite Launch CenterSichuan Province🇨🇳 China
Palmachim AirbaseCentral District🇮🇱 Israel
Imam Khomeini Space Launch TerminalSemnan🇮🇷 Iran
Qom Lauch FacilityQom🇮🇷 Iran
El Arenosillo Test CentreHuelva🇪🇸 Spain
Spaceport SwedenLapland🇸🇪 Sweden
Esrange Space CenterLapland🇸🇪 Sweden
Andøya SpaceNordland🇳🇴 Norway
SaxaVord SpaceportShetland Islands🇬🇧 UK
Sutherland SpaceportSutherland🇬🇧 UK
Western Isles SpaceportOuter Hebrides🇬🇧 UK
Spaceport MachrihanishCampbeltown🇬🇧 UK
Prestwick SpaceportGlasgow🇬🇧 UK
Snowdonia SpaceportNorth West Wales🇬🇧 UK
Spaceport CornwallCornwall🇬🇧 UK
Orbex LP1Moray🇬🇧 UK
Spaceport Nova ScotiaNova Scotia🇨🇦 Canada

Editor’s note: The above table includes all sites that are operational, as well as under construction, as of publishing date.

The list above covers fixed locations, and does not include SpaceX’s autonomous spaceport drone ships. There are currently three active drone ships—one based near Los Angeles, and the other two based at Port Canaveral, Florida.

Two of the most famous launch sites on the list are the Baikonur Cosmodrome (Kazakhstan) and Cape Canaveral (United States). The former was constructed as the base of operations for the Soviet space program and was the launch point for Earth’s first artificial satellite, Sputnik 1. The latter was NASA’s primary base of operations and the first lunar-landing flight was launched from there in 1969.

The global roster of spaceports has grown immensely since Baikonur and Cape Canaveral were the only game in town. Now numerous countries have the ability to launch satellites, and many more are getting in on the action.

Wenchang Space Launch Site, on the island of Hainan, is China’s newest launch location. The site recorded its first successful launch in 2016.

Location, Location

One interesting quirk of the map above is the lack of spaceports in Europe. Europe’s ambitions for space are actually launched from the Guiana Space Centre in South America. Europe’s Spaceport has been operating in French Guiana since 1968.

Low altitude launch locations near the equator are the most desirable, as far less energy is required to take a spacecraft from surface level to an equatorial, geostationary orbit.

Islands and coastal areas are also common locations for launch sites. Since the open waters aren’t inhabited, there is minimal risk of harm from debris in the event of a launch failure.

As demand for satellites and space exploration grows, the number of launch locations will continue to grow as well.

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Charted: The Exponential Growth in AI Computation

In eight decades, artificial intelligence has moved from purview of science fiction to reality. Here’s a quick history of AI computation.



A cropped version of the time series chart showing the creation of machine learning systems on the x-axis and the amount of AI computation they used on the y-axis measured in FLOPs.

Charted: The Exponential Growth in AI Computation

Electronic computers had barely been around for a decade in the 1940s, before experiments with AI began. Now we have AI models that can write poetry and generate images from textual prompts. But what’s led to such exponential growth in such a short time?

This chart from Our World in Data tracks the history of AI through the amount of computation power used to train an AI model, using data from Epoch AI.

The Three Eras of AI Computation

In the 1950s, American mathematician Claude Shannon trained a robotic mouse called Theseus to navigate a maze and remember its course—the first apparent artificial learning of any kind.

Theseus was built on 40 floating point operations (FLOPs), a unit of measurement used to count the number of basic arithmetic operations (addition, subtraction, multiplication, or division) that a computer or processor can perform in one second.

ℹ️ FLOPs are often used as a metric to measure the computational performance of computer hardware. The higher the FLOP count, the higher computation, the more powerful the system.

Computation power, availability of training data, and algorithms are the three main ingredients to AI progress. And for the first few decades of AI advances, compute, which is the computational power needed to train an AI model, grew according to Moore’s Law.

PeriodEraCompute Doubling
1950–2010Pre-Deep Learning18–24 months
2010–2016Deep Learning5–7 months
2016–2022Large-scale models11 months

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

However, at the start of the Deep Learning Era, heralded by AlexNet (an image recognition AI) in 2012, that doubling timeframe shortened considerably to six months, as researchers invested more in computation and processors.

With the emergence of AlphaGo in 2015—a computer program that beat a human professional Go player—researchers have identified a third era: that of the large-scale AI models whose computation needs dwarf all previous AI systems.

Predicting AI Computation Progress

Looking back at the only the last decade itself, compute has grown so tremendously it’s difficult to comprehend.

For example, the compute used to train Minerva, an AI which can solve complex math problems, is nearly 6 million times that which was used to train AlexNet 10 years ago.

Here’s a list of important AI models through history and the amount of compute used to train them.

Perceptron Mark I1957–58695,000
Neocognitron1980228 million
NetTalk198781 billion
TD-Gammon199218 trillion
NPLM20031.1 petaFLOPs
AlexNet2012470 petaFLOPs
AlphaGo20161.9 million petaFLOPs
GPT-32020314 million petaFLOPs
Minerva20222.7 billion petaFLOPs

Note: One petaFLOP = one quadrillion FLOPs. Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

The result of this growth in computation, along with the availability of massive data sets and better algorithms, has yielded a lot of AI progress in seemingly very little time. Now AI doesn’t just match, but also beats human performance in many areas.

It’s difficult to say if the same pace of computation growth will be maintained. Large-scale models require increasingly more compute power to train, and if computation doesn’t continue to ramp up it could slow down progress. Exhausting all the data currently available for training AI models could also impede the development and implementation of new models.

However with all the funding poured into AI recently, perhaps more breakthroughs are around the corner—like matching the computation power of the human brain.

Where Does This Data Come From?

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

Note: The time estimated to for computation to double can vary depending on different research attempts, including Amodei and Hernandez (2018) and Lyzhov (2021). This article is based on our source’s findings. Please see their full paper for further details. Furthermore, the authors are cognizant of the framing concerns with deeming an AI model “regular-sized” or “large-sized” and said further research is needed in the area.

Methodology: The authors of the paper used two methods to determine the amount of compute used to train AI Models: counting the number of operations and tracking GPU time. Both approaches have drawbacks, namely: a lack of transparency with training processes and severe complexity as ML models grow.

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