Google Maps with Machine Learning

Machine learning has succeeded in increasing the standard and convenience of people’s transportation. Google Maps is another main machine learning tool that enhances human standards. Today, we are using this app to find the shortest routes and nearby places like restaurants, ATMs, and hotels.

The app has been designed with statistical models and algorithms to perform tasks that are not programmed. The machine learning algorithm built a mathematical model based on sample data to achieve this.

Google Maps has millions of users who rely on the app to provide them with real-time location information. To keep this information up to date, the app has to analyze 80 billion images. Doing this manually would be a challenging task, which is why machine learning is used to help achieve this goal. Data is the backbone for developing machine learning algorithms.

Machine learning algorithms power features like satellite image gallery, street maps, 360-degree interactive panoramic view, and real-time traffic conditions.

Business entities across the world choose the Google Map platform due to the freshness of its point of interest (POI) Data. AI and machine learning capabilities of Google map are constantly improving to ensure that we keep their POI information up-to-date.

Artificial intelligence and machine learning have helped to increase the quality and accuracy of the map. Using machine learning models on Street View images, for example, has helped enhance the quality of address data accessible to clients on Google Maps Platform, resulting in more accurate geospatial experiences for their end users.

Future of Google Map

Machine learning models aim to minimize errors in travel estimations. However, DeepMind researchers have discovered that utilizing a linear combination of multiple loss functions significantly improves the model’s ability to generalize. One of the most notable loss functions is MetaGradient, which was a result of DeepMind’s thorough research into reinforcement learning

ML with Google Maps

Machine learning (ML) is critical in improving Google Maps’ functioning and capabilities. Google Maps uses ML approaches to increase accuracy, give real-time information, and provide users with personalized experiences.

· Route Optimization

To get more accurate and fastest routes, ML helps by considering real-time traffic data, historical traffic patterns, road closures, and water conditions.

· Traffic Prediction

To create more effective traffic conditions, they have used historical data on traffic conditions and also active mobile devices available on the particular route. Further, the speed of the users can be calculated through the sensors available on the mobile device.

· Location Recommendations

Location recommendations are suggested based on the nearby places, businesses, and points of interest based on the users’ current location, search history, and user preference. Further, these personalized recommendation systems enhance the user experience.

· Street View

ML is essential for processing and enhancing images on Google Street View. Advanced image recognition algorithms are utilized to confidently identify and label street names, shops, and various landmarks with precision.

· Local Guides and Reviews

Google Maps effectively uses cutting-edge machine learning algorithms to efficiently detect and eliminate any false reviews and ratings. Furthermore, it actively incentivizes its users, particularly the Local Guides, who play a vital role in providing valuable information about local attractions, uploading images, and writing honest reviews.

Referencing

· Gupta, S. (2021). Understanding AI and Machine Learning Algorithms in Google Maps. [online] Springboard Blog. Available at: https://www.springboard.com/blog/data-science/machine-learning-google-maps/.

· ‌Ali, W. (2023). 7 Real-Life Applications of Machine Learning. [online] MUO. Available at: https://www.makeuseof.com/real-life-applications-machine-learning/ [Accessed 2 Sep. 2023].

· ‌Lau, J. (2020). Google Maps 101: How AI helps predict traffic and determine routes. [online] Google. Available at: https://blog.google/products/maps/google-maps-101-how-ai-helps-predict-traffic-and-determine-routes/.

· ‌Sharma, S., Shakya, H.K. and Marriboyina, V. (2021). A location based novel recommender framework of user interest through data categorization. Materials Today: Proceedings, 47, pp.7155–7161. doi:https://doi.org/10.1016/j.matpr.2021.06.325.

· ‌Social Media Today. (n.d.). Google Develops Machine Learning System to Recognize Business Names from Street View Images. [online] Available at: https://www.socialmediatoday.com/technology-data/google-develops-machine-learning-system-recognize-business-names-street-view-images [Accessed 2 Sep. 2023].

# Chameera De Silva