I was born two years before the USSR ceased to exist. The largest country in the world disappeared overnight, replaced by the new largest country in the world, Russia. But the footprint it left took longer to be washed away. I grew up with a duvet cover printed with a world map prominently featuring the ex-nation, reading books and atlases that were published after I was born but before it vanished, and voraciously consuming science fiction that assumed the Soviets would continue to exist far into the future. Randall Munroe, author of the webcomic XKCD, once put together a flow chart to date almost any world map made since the 19th century to within a few years by answering some simple questions.

In recent years, the rapid development of high-precision map technology combined with artificial intelligence has ushered in a new development opportunity in the field of intelligent vehicles. High-precision map technology is an important guarantee for intelligent vehicles to achieve autonomous driving. However, due to the lack of research on high-precision map technology, it is difficult to rationally use this technology in the field of intelligent vehicles. Therefore, relevant researchers studied a fast and effective algorithm to generate high-precision GPS data from a large number of low-precision GPS trajectory data fusion, and generated several key data points to simplify the description of GPS trajectory, and realized the "crowdsourced update" model based on a large number of social vehicles for map data collection came into being. This kind of algorithm has the important significance to improve the data accuracy, reduce the measurement cost and reduce the data storage space. On this basis, this paper analyzes the implementation form of crowdsourcing map, so as to improve the various information data in the high-precision map according to the actual situation, and promote the high-precision map can be reasonably applied to the intelligent car.

Electricity is the energy that drives modern society but trying to figure out where cables are located continues to provide engineers with major challenges when considering that these cables were installed decades ago. Now, Google is using AI to automatically generate maps of where all cables are so that future engineers know precisely how to make upgrades and repairs. Why are cable networks providing engineers with significant challenges, what will Google's new project do, and should this idea of digitised cables be brought into homes? If there is one fact of life that I am truly grateful is that electricity comes out of my sockets whenever I require it. Just like the Armstrong and Miller Time Traveller sketch, the number of people in society who are entirely oblivious to the effort and resources needed to correctly operate a nationwide grid is astonishing (electricity is that thing that comes out of the wall).

Geoscape Australia, a government-owned geospatial data company, has announced it has partnered with an Israeli artificial intelligence start-up to use machine vision and deep learning technology to enhance its 3D digital maps of Australia. The CEO of Geoscape Australia said that the partnership will advance what is known about every address across the country. Applying the Israeli AI start-up's patented AI technology to the highest quality aerial imagery will significantly evolve the current digital model of Australia. The company says more accurate digital models of Australia's urban environment will enable the data-driven foundation of Digital Twin applications that better reflect the real world. The up-to-date data will also improve the assessment of risk for insurers, allow architects to visualise new developments in the context of their surroundings, help noise modellers better understand what will be impacted by noise, and power modelling of energy use patterns in commercial and residential buildings.

Many young urbanites don't want to own a car, and unlike earlier generations, they don't have to rely on mass transit. Instead they treat mobility as a service: When they need to travel significant distances, say, more than 5 miles (8 kilometers), they use their phones to summon an Uber (or a car from a similar ride-sharing company). If they have less than a mile or so to go, they either walk or use various "micromobility" services, such as the increasingly ubiquitous Lime and Bird scooters or, in some cities, bike sharing. The problem is that today's mobility-as-a-service ecosystem often doesn't do a good job covering intermediate distances, say a few miles. Hiring an Uber or Lyft for such short trips proves frustratingly expensive, and riding a scooter or bike more than a mile or so can be taxing to many people.

Imagine Martha, an octogenarian who lives independently and uses a wheelchair. All objects in her home are digitally catalogued; all sensors and the devices that control objects have been Internet-enabled; and a digital map of her home has been merged with the object map. As Martha moves from her bedroom to the kitchen, the lights switch on, and the ambient temperature adjusts. The chair will slow if her cat crosses her path. When she reaches the kitchen, the table moves to improve her access to the refrigerator and stove, then moves back when she is ready to eat.

The Anatomy of an AI project, created and designed by Kate Crawford and Vladan Joler, consists of a website, digital publication, physical publication and digital map. Published online in September 2018, the project responds to the technical and human infrastructure behind Amazon's voice assistant'Alexa' and the Amazon Echo device that it is enabled by. Often with the case of proprietary or closed technology such as the Amazon Alexa, it is difficult to see the infrastructure and technology behind these and other similar voice assistants. The Anatomy of an AI System project shows in detail – a result of extensive research by technology and social science researchers, as well as Joler and Crawford – the complex journey through which digitally-enabled consumer devices come to exist in society. This digital poster, to be printed at a minimum scale of 220x360 cm, is a contemporary record of obscured, inaccessible digital and corporate processes.

A model invented by researchers at MIT and Qatar Computing Research Institute (QCRI) that uses satellite imagery to tag road features in digital maps could help improve GPS navigation. Showing drivers more details about their routes can often help them navigate in unfamiliar locations. Lane counts, for instance, can enable a GPS system to warn drivers of diverging or merging lanes. Incorporating information about parking spots can help drivers plan ahead, while mapping bicycle lanes can help cyclists negotiate busy city streets. Providing updated information on road conditions can also improve planning for disaster relief.

In-car satnav systems and mobile mapping apps have made it much easier to travel from one place to another without getting lost, but a new innovation promises to help fix a remaining pain point – getting in the right lane at intersections. Today's mapping apps aren't always much help if you're at an unfamiliar intersection and aren't sure exactly where on the road your car is supposed to be: the apps often don't have the detail or the knowledge to warn you in good time about changing lanes. The system developed by researchers at MIT and the Qatar Computing Research Institute uses satellite imagery to augment existing mapping data, but the smart part is applying artificial intelligence to work out the layout of roads hidden by trees and buildings. It's called RoadTagger, and by deploying machine learning on satellite imagery, the system is able to figure out with a high degree of accuracy some extra details on roads – including, for example, how many lanes they have. That could give drivers an early warning about diverging or merging lanes.

A model invented by researchers at MIT and Qatar Computing Research Institute (QCRI) that uses satellite imagery to tag road features in digital maps could help improve GPS navigation. Showing drivers more details about their routes can often help them navigate in unfamiliar locations. Lane counts, for instance, can enable a GPS system to warn drivers of diverging or merging lanes. Incorporating information about parking spots can help drivers plan ahead, while mapping bicycle lanes can help cyclists negotiate busy city streets. Providing updated information on road conditions can also improve planning for disaster relief.