Google map is probably the app which we use every time we go out. A couple of days ago, I was driving to other parts of the city with google maps guiding me on my side. I was traveling somewhere around the outer ring road when the map voice said despite the usual traffic, you are on the fastest route, but how does the google map know that?
Today, most of our searches on the internet land on an online map for directions, be it a restaurant, a store, a bus stand, or a clinic. It has become our virtual compass to finding our way through densely populated cities or even remote pathways. Google Maps is one of the most accurate and detailed maps available today. Have you ever wondered what makes it so accurate? Have you ever thought about how it knows the location of a new restaurant or the junction where there is a roadblock? Well, the technology behind this mastermind is Machine Learning (ML).
WHAT IS MACHINE LEARNING?
Machine Learning (ML) is a branch of AI (Artificial intelligence) that provides applications the ability to learn through experience. It uses statistical models and algorithms to perform tasks that are not explicitly programmed. To perform a task, Machine Learning Algorithms build a mathematical model based on sample data.
However, if a programmer has to write a program to do what our mind does, it would be impossible to do so. This is where Machine Learning is more practical. You don’t need to tell the Machine Learning Algorithms what to do and how to do it. Instead, you provide the algorithm with lots of examples (in this case, image) and the algorithm learns from these examples.
Machine Learning in Google Maps
Google Maps provides useful directions and real-time traffic information to millions of users. This information is updated constantly to mirror the changes in an ever-changing world. It is impossible to manually analyze more than 80 billion images to find new or updated information for Google Maps. One of the goals of Machine Learning is to enable the automatic extraction of information from geo-located imagery to improve Google Maps.
Do you know?
Google has partnered with DeepMind, an Alphabet AI research lab, to improve the accuracy of its traffic prediction capabilities. “Our ETA predictions already have a very high accuracy bar, in fact, we see that our predictions have been consistently accurate for over 97 percent of trips,” said Johann Lau, Product Manager, Google Maps.
Imagery and authoritative data are static and can’t keep up with the ever-changing world around us. Machine Learning algorithms can analyze existing images and data and identify changes in the new data. Thus, the maps are updated with only the recent changes. This increases the speed of mapping and allows for automation of mapping processes while maintaining accuracy.
It makes use of a deep neural network that automates the image information reading process. This algorithm is publicly available on GitHub through TensorFlow, which is Google’s own open-source machine learning software library.
Google is already implementing machine learning to identify car license plates. And now, it is using the same technology to fetch information from street signs. Using this technology, Google aims to improve the location data of about one-third of the world’s addresses.
The latest Machine Learning algorithms helped achieve 84.2 % accuracy when tested on several challenging street signs in France. These statistics significantly outperformed the previous state-of-the-art systems. This move improved the software to read the street numbers and street names. The new algorithm can get rid of any irrelevant text in its photos and replace abbreviations with their full names.
So how exactly does this all work in real life?
Say you’re heading to a job interview across town, driving down the road you typically take to get there. When you leave the house, traffic is flowing freely with zero indication of any disruptions along the way. With Google Maps’ traffic predictions combined with live traffic conditions, we let you know that if you continue down your current route, there’s a good chance you’ll get stuck in unexpected gridlock traffic about 30 minutes into your ride which would mean missing your job interview. As a result, Google Maps automatically reroutes you using its knowledge about nearby road conditions and incidents helping you avoid the jam altogether and get to your job interview on time.
HERE ARE SOME LATEST GOOGLE MAPS TRENDS
- Drive conversion with google maps platform retail solution
- Show customers the best store or restaurant to visit
- Simply checkout and increase sales
- Reimagine a seamless retail experience
- Announcing Version 4.0 of the Maps and Places SDKs for IOS
- IOS 10+
- Improved Swift support
- Removing deprecations
- Add a more accurate sense of place to your applications using these five YouTube tutorials