Big data gaming: the paradigm shift has come

 Hello and welcome to Open Citizen Data Science!

While gaming usually isn't something easily associated with Data Science, 2020 is bringing huge changes in a business that is still often seen as child's play.

Data Science isn't just numbers for a presentation anymore

The gaming world right now has most eye fixed on the gaming console generation change, however while it will be a significant technological jump with vastly improved performance (and also a focus on faster data access), that's an evolutionary change rather than a revolution.

The real change is instead coming from Flight Simulator 2020, a relatively niche game, from a definitely not niche company: Microsoft.
The genre and the franchise are not new and have been around for the better part of the last 40 years (the first Flight Simulator is from 1982), however the latest edition brings something no game has ever done before: an unique blend of real world data and AI content generation

Big Data and data science for content generation

Flight Simulator 2020 is not the first game that uses real-world map data or to use AI to generate gaming environments. Procedural content generation is nothing new and has been used to create entire universes in real time before.

The difference here is in the blend of several data sources to reproduce the world as we know it in a simulated environment instead of an abstraction. How has this been possible?

Through data, made available in extreme amounts and from different sources and using many parts of the data science stack as we know it.

Flight Simulator's world generation has its foundation on Bing Maps data: over 2 petabytes of data that are accessed in real-time from the cloud as the players fly through the world.
This allows an high degree of fidelity in recreating city and terrain layouts and is the first layer of real-world data used, while a second layer comes from satellite and fly-by pictures, giving access to photogrammetry data.

The blend of those two sources is fed to an Azure machine learning environment, which is used to classify buildings, landmarks, and environment types from trees to building materials, which in turn generates multiple terabytes of textures and height map data.

Strategic partnerships for external data and AI optimisations

This isn't the only source available to the game. To ensure maximum fidelity, Microsoft partnered with other companies, employing specific AI and real-time data optimisations.

Blackshark.ai provided the algorithms for content generation, turning raw data into objects:

Through deep learning, content is generated on Azure servers and streamed in real-time to the user, recreating every single building and object detected via Bing Maps as realistically as possible, literally enabling the player to find his own home in the game if he wishes.

Meteoblue provides real-time weather data:

This allows for an extremely accurate weather simulation, so that if it's raining in a particular location the player will experience the same condition in-game (with the option of custom weather as well).
They also provide a very transparent weather forecast, where you can see their averaged model or what has been predicted by any single weather model for any location.

Finally, VATSIM is used to provide real-time air traffic control feedback:

This will allow for realistic ATC feedback in the game, making the experience even more immersive.

Raising the bar and creating new business cases

For all its AI prowess, the game is of course not a 100% faithful reproduction of the real world and especially in smaller locations the algorithms are going to fail to properly recreate the environment.
Microsoft itself acknowledges this and several airports have been manually optimised in order to ensure maximum realism. 

However, this represents a major shift in expectations: instead of navigating a virtual world and looking for similarities with the real world, players will be able to get in and look for differences, while the general world is going to be an accurate representation of reality. 

This enables use cases that were not imaginable before: while simulators were always used for training, one can easily imagine such a software being employed by travel agencies to give previews of touristic locations: an immersive view of the world with a freedom never possible before (and something that 20 years ago would have been a dream in a world of quicktime-powered software that tried to recreate virtual visits).

Similar scenarios could be applied in logistics (real-time traffic data is available for route optimization) training and I'm sure our readers could imagine something closer to their domain.

Personally, I will await with trepidation more examples of what we could call Big Data Gaming both as an analyst and as a gamer, where I haven't seen something this revolutionary since the advent of 3D accelerators, which are now also the GPUs fueling the deep learning algorithms of most major players.

This concludes our article, stay tuned for new content!