Google has not upgraded their Street View cameras for eight years, and at the pace in which technology progresses, that is like an eternity. On the other hand, we are talking about Google here, and they don’t do anything halfway. The new-and-improved Street View cameras can do much more than take photos of its surroundings. The upgraded cameras can relay detailed information to Google’s machine-learning algorithm, which will then analyze and utilize that data to improve all of the company’s platforms. Let’s take a look at some of the specific advancements that were implemented into the new cameras.
First, the number of cameras placed on top of Google’s fleet of vehicles has been reduced from 15 to seven; however, the camera resolution has been upgraded considerably. The high-res cameras should provide clearer, more detailed photos of its surroundings. Of course, Google’s mapping is still used for its GPS functionality; however, the broader questions that are being asked of Google has forced the forward-thinking company to accommodate the “need it now” generation. For example, back when Google started using Street View for mapping, users only requested directions to their residence or to popular landmarks like the Eiffel Tower or Stonehenge. Now, people are asking for directions to an Italian restaurant that is open on Sunday and caters to large crowds. The new cameras can zero in on company names, hours of operations, colors or building, and more. All of this information can be used to answer questions like that and provide the best information for Google’s customers. Eventually, Google would like to be able to answer an inquiry as broad as the name of the business located in the blue building down the street.
Other Street View Data
In addition to the advancement of Google’s GPS capabilities, their machine-learning algorithm can use the new Street View imagery to assess the details within each photo and predict things like estimated household income, hobbies, and other demographics that might represent the people of that community. For example, photos of vehicles that are parked in a person’s driveway can be used to gather the make, model, and year of each automobile. Using that information, Google can compare the data gathered from a specific neighborhood to gain a better understanding of that community such as socioeconomic attributes and political preferences. The detail-oriented algorithm will notice political stickers on cars or the amount of homes that have a basketball hoop to better understand the neighborhood, in order to provide the best information for users that reside there.