Or when an area has lots of tree cover that makes it hard to see roads underneath, or no tree cover but dry riverbeds that look like dirt roads.
And then there are economic differences like how quickly new roads and buildings are constructed, and how quickly new businesses open up. Add in the fact of different languages and different scripts that our algorithms, machine learning and human operators need to understand, and you have a lot of complicating factors leading to different kinds of problems in different parts of the world.
To address these differences we take new and different mapping approaches to these areas. For an area with few authoritative data sources to reference, we use satellite and street-level imagery and machine learning to identify roads or businesses and add the information to our maps data. This happens especially with things that are moving, like a person walking or an airplane flying.
The world is a vast and constantly changing place. Think about how frequently restaurants in your neighborhood come and go, and then consider all the businesses, buildings, homes and roads that are built—and then scale that up to more than countries and regions that are home to more than 7 billion people in the world.
Also see: How Google Maps is using machine learning to ease our parking woes. Then again, data and imagery are static. Google Maps imagery, especially, can be anywhere from 1 to 3 years old even older, in some cases.
And these components alone do not always give users the context they need about a specific place on the map. This is where real people enter the picture. Though any new piece of information is updated only after ensuring a high degree of confidence in its accuracy, Google Maps receives more than 20 million inputs from its users every day. This means, in essence, Google Maps is updated constantly — literally, every second of every day!
Today, Google Maps contain information, ratings, and reviews of about million places around the world. But what happens when a building does not have a traditional address. A Plus Code is an address based on the latitude and longitude coordinates of a location. Each code is 10 characters long and consists of two parts: an area code roughly x kilometers and a local code about 14 x 14 meters.
The code can also be combined with a locality. Well, you can find the dates easily both in Google Maps and Google Earth. If you wish to know the date when satellites captured those aerial images that you see in Google Maps, you will have to use Google Earth for that. Launch the Google Earth app on your desktop, search for any location in the sidebar and, this is important, zoom in an area as much as possible. Now hover your mouse over the map and you should see the capture date of that satellite image in the status bar as seen in the above screenshot.
If you happen to live in a country where Google Street View is available, you can use the Google Maps website itself to determine the date when Google Street Views cars were in your area capturing pictures of the neighbourhood.
Go to maps. The image capture date will be instantly displayed in the status bar as shown in the screenshot below. When we say pieces, we mean small pieces. A single Google Earth update might contain a handful of cities or states. Both of these take time and aerial photographs in particular are expensive to acquire. Google would have to have hired pilots traversing the globe all of the time to keep up with potential changes. Instead, Google opts for a compromise.
They strive to keep each area of the globe within 3 years of age.
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