- Explore: Looking for a place nearby to grab lunch, enjoy live music or play arcade games? In the Explore tab, you’ll find information, ratings, reviews and more for about 200 million places around the world, including local restaurants, nearby attractions and city landmarks.
- Commute: Whether you’re traveling by car or public transit, the Commute tab is there to make sure you’re on the most efficient route. Set up your daily commute to get real-time traffic updates, travel times and suggestions for alternative routes.
- Saved: People have saved more than 6.5 billion places on Google Maps—from the new bakery across town to the famous restaurant on your upcoming vacation. Now you can view all of these spots in one convenient place, as well as find and organize plans for an upcoming trip and share recommendations based on places you've been.
- Contribute: Hundreds of millions of people each year contribute information that helps keep Google Maps up to date. With the new Contribute tab, you can easily share local knowledge, such as details about roads and addresses, missing places, business reviews and photos. Each contribution goes a long way in helping others learn about new places and decide what to do.
- Updates: The new Updates tab provides you with a feed of trending, must-see spots from local experts and publishers, like The Infatuation. In addition to discovering, saving and sharing recommendations with your network, you can also directly chat with businesses to get questions answered.
- Temperature: For a more comfortable ride, check in advance if the temperature is considered by past riders as on the colder or warmer side.
- Accessibility: If you have special needs or require additional support, you can identify public transit lines with staffed assistance, accessible entrance and seating, accessible stop-button or hi-visible LED.
- Women’s Section: In regions where transit systems have designated women's sections or carriages, we'll help surface this information along with whether other passengers abide by it.
- Security Onboard: Feel safer knowing if security monitoring is on board—whether that’s with a security guard present, installed security cameras or an available helpline.
- Number of carriages available: In Japan only, you can pick a route based on the number of carriages so that it increases your chances of getting a seat.
The time has come!
We’re excited to announce the official launch of the Google Maps Platform YouTube channel, a place for developers to learn and immerse themselves in the possibilities with maps.
We already have some great content on the channel about how to get started, incredible user stories, and the return of our Geocasts series coming soon - a series dedicated to providing walkthroughs and tips to help you learn how to implement Google Maps Platform features in your web and mobile apps.Subscribe here → @GoogleMapsPlatformSee you there!
Source: Google Developers Blog
One of the consistent challenges when navigating with Google Maps is figuring out the right direction to go: sure, the app tells you to go north - but many times you're left wondering, "Where exactly am I, and which way is north?" Over the years, we've attempted to improve the accuracy of the blue dot with tools like GPS and compass, but found that both have physical limitations that make solving this challenge difficult, especially in urban environments.
We're experimenting with a way to solve this problem using a technique we call global localization, which combines Visual Positioning Service (VPS), Street View, and machine learning to more accurately identify position and orientation. Using the smartphone camera as a sensor, this technology enables a more powerful and intuitive way to help people quickly determine which way to go.
|Due to limitations with accuracy and orientation, guidance via GPS alone is limited in urban environments. Using VPS, Street View and machine learning, Global Localization can provide better context on where you are relative to where you're going.|
Where GPS Falls Short
The process of identifying the position and orientation of a device relative to some reference point is referred to as localization. Various techniques approach localization in different ways. GPS relies on measuring the delay of radio signals from multiple dedicated satellites to determine a precise location. However, in dense urban environments like New York or San Francisco, it can be incredibly hard to pinpoint a geographic location due to low visibility to the sky and signals reflecting off of buildings. This can result in highly inaccurate placements on the map, meaning that your location could appear on the wrong side of the street, or even a few blocks away.
|GPS signals bouncing off facades in an urban environment.|
A New Approach to Localization
To improve the precision position and orientation of the blue dot on the map, a new complementary technology is necessary. When walking down the street, you orient yourself by comparing what you see with what you expect to see. Global localization uses a combination of techniques that enable the camera on your mobile device to orient itself much as you would.
VPS determines the location of a device based on imagery rather than GPS signals. VPS first creates a map by taking a series of images which have a known location and analyzing them for key visual features, such as the outline of buildings or bridges, to create a large scale and fast searchable index of those visual features. To localize the device, VPS compares the features in imagery from the phone to those in the VPS index. However, the accuracy of localization through VPS is greatly affected by the quality of the both the imagery and the location associated with it. And that poses another question—where does one find an extensive source of high-quality global imagery?
Enter Street View
Over 10 years ago we launched Street View in Google Maps in order to help people explore the world more deeply. In that time, Street View has continued to expand its coverage of the world, empowering people to not only preview their route, but also step inside famous landmarks and museums, no matter where they are. To deliver global localization with VPS, we connected it with Street View data, making use of information gathered and tested from over 93 countries across the globe. This rich dataset provides trillions of strong reference points to apply triangulation, helping more accurately determine the position of a device and guide people towards their destination.
|Features matched from multiple images.|
Combining Global Localization with Augmented Reality
Global localization is an additional option that users can enable when they most need accuracy. And, this increased precision has enabled the possibility of a number of new experiences. One of the newest features we're testing is the ability to use ARCore, Google's platform for building augmented reality experiences, to overlay directions right on top of Google Maps when someone is in walking navigation mode. With this feature, a quick glance at your phone shows you exactly which direction you need to go.
Local Guides, a small group of Google Maps enthusiasts around the world who we know will offer us the feedback about how this approach can be most helpful.
Like other AI-driven camera experiences such as Google Lens (which uses the camera to let you search what you see), we believe the ability to overlay directions over the real world environment offers an exciting and useful way to use the technology that already exists in your pocket. We look forward to continuing to develop this technology, and the potential for smartphone cameras to add new types of valuable experiences.
Source: Google AI Blog
Posted by Google Maps Platform Team
It's been thirteen years since we opened up Google Maps to your creativity and passion. Since then, it's been exciting to see how you've transformed your industries and improved people's lives. You've changed the way we ride to work, discover the best schools for our children, and search for a new place to live. We can't wait to see what you do next. That's why today we're introducing a series of updates designed to make it easier for you to start taking advantage of new location-based features and products.
We're excited to announce Google Maps Platform—the next generation of our Google Maps business—encompassing streamlined API products and new industry solutions to help drive innovation.
In March, we announced our first industry solution for game studios to create real-world games using Google Maps data. Today, we also offer solutions tailored for ridesharing and asset tracking companies. Ridesharing companies can embed the Google Maps navigation experience directly into their apps to optimize the driver and customer experience. Our asset tracking offering helps businesses improve efficiencies by locating vehicles and assets in real-time, visualizing where assets have traveled, and routing vehicles with complex trips. We expect to bring new solutions to market in the future, in areas where we're positioned to offer insights and expertise.
Our core APIs work together to provide the building blocks you need to create location-based apps and experiences. One of our goals is to evolve our core APIs to make them simpler, easier to use and scalable as you grow. That's why we've introduced a number of updates to help you do so.
Streamlined products to create new location-based experiences
We're simplifying our 18 individual APIs into three core products—Maps, Routes and Places, to make it easier for you to find, explore and add new features to your apps and sites. And, these new updates will work with your existing code—no changes required.
One pricing plan, free support, and a single console
We've heard that you want simple, easy to understand pricing that gives you access to all our core APIs. That's one of the reasons we merged our Standard and Premium plans to form one pay-as-you go pricing plan for our core products. With this new plan, developers will receive the first $200 of monthly usage for free. We estimate that most of you will have monthly usage that will keep you within this free tier. With this new pricing plan you'll pay only for the services you use each month with no annual, up-front commitments, termination fees or usage limits. And we're rolling out free customer support for all. In addition, our products are now integrated with Google Cloud Platform Console to make it easier for you to track your usage, manage your projects, and discover new innovative Cloud products.
Scale easily as you grow
Beginning June 11, you'll need a valid API key and a Google Cloud Platform billing account to access our core products. Once you enable billing, you will gain access to your $200 of free monthly usage to use for our Maps, Routes, and Places products. As your business grows or usage spikes, our plan will scale with you. And, with Google Maps' global infrastructure, you can scale without thinking about capacity, reliability, or performance. We'll continue to partner with Google programs that bring our products to nonprofits, startups, crisis response, and news media organizations. We've put new processes in place to help us scale these programs to hundreds of thousands of organizations and more countries around the world.
We're excited about all the new location-based experiences you'll build, and we want to be there to support you along the way. If you're currently using our core APIs, please take a look at our Guide for Existing Users to further understand these changes and help you easily transition to the new plan. And if you're just getting started, you can start your first project here. We're here to help.
Source: Google Developers Blog
Solving large-scale optimization problems often starts with graph partitioning, which means partitioning the vertices of the graph into clusters to be processed on different machines. The need to make sure that clusters are of near equal size gives rise to the balanced graph partitioning problem. In simple terms, we need to partition the vertices of a given graph into k almost equal clusters, while we minimize the number of edges that are cut by the partition. This NP-hard problem is notoriously difficult in practice because the best approximation algorithms for small instances rely on semidefinite programming which is impractical for larger instances.
This post presents the distributed algorithm we developed which is more applicable to large instances. We introduced this balanced graph-partitioning algorithm in our WSDM 2016 paper, and have applied this approach to several applications within Google. Our more recent NIPS 2017 paper provides more details of the algorithm via a theoretical and empirical study.
Balanced Partitioning via Linear Embedding
Our algorithm first embeds vertices of the graph onto a line, and then processes vertices in a distributed manner guided by the linear embedding order. We examine various ways to find the initial embedding, and apply four different techniques (such as local swaps and dynamic programming) to obtain the final partition. The best initial embedding is based on “affinity clustering”.
Affinity Hierarchical Clustering
Affinity clustering is an agglomerative hierarchical graph clustering based on Borůvka’s classic Maximum-cost Spanning Tree algorithm. As discussed above, this algorithm is a critical part of our balanced partitioning tool. The algorithm starts by placing each vertex in a cluster of its own: v0, v1, and so on. Then, in each iteration, the highest-cost edge out of each cluster is selected in order to induce larger merged clusters: A0, A1, A2, etc. in the first round and B0, B1, etc. in the second round and so on. The set of merges naturally produces a hierarchical clustering, and gives rise to a linear ordering of the leaf vertices (vertices with degree one). The image below demonstrates this, with the numbers at the bottom corresponding to the ordering of the vertices.
NIPS’17 paper explains how we run affinity clustering efficiently in the massively parallel computation (MPC) model, in particular using distributed hash tables (DHTs) to significantly reduce running time. This paper also presents a theoretical study of the algorithm. We report clustering results for graphs with tens of trillions of edges, and also observe that affinity clustering empirically beats other clustering algorithms such as k-means in terms of “quality of the clusters”. This video contains a summary of the result and explains how this parallel algorithm may produce higher-quality clusters even compared to a sequential single-linkage agglomerative algorithm.
Comparison to Previous Work
In comparing our algorithm to previous work in (distributed) balanced graph partitioning, we focus on FENNEL, Spinner, METIS, and a recent label propagation-based algorithm. We report results on several public social networks as well as a large private map graph. For a Twitter followership graph, we see a consistent improvement of 15–25% over previous results (Ugander and Backstrom, 2013), and for LiveJournal graph, our algorithm outperforms all the others for all cases except k = 2, where ours is slightly worse than FENNEL's.
The following table presents the fraction of cut edges in the Twitter graph obtained via different algorithms for various values of k, the number of clusters. The numbers given in parentheses denote the size imbalance factor: i.e., the relative difference of the sizes of largest and smallest clusters. Here “Vanilla Affinity Clustering” denotes the first stage of our algorithm where only the hierarchical clustering is built and no further processing is performed on the cuts. Notice that this is already as good as the best previous work (shown in the first two columns below), cutting a smaller fraction of edges while achieving a perfect (and thus better) balance (i.e., 0% imbalance). The last column in the table includes the final result of our algorithm with the post-processing.
We apply balanced graph partitioning to multiple applications including Google Maps driving directions, the serving backend for web search, and finding treatment groups for experimental design. For example, in Google Maps the World map graph is stored in several shards. The navigational queries spanning multiple shards are substantially more expensive than those handled within a shard. Using the methods described in our paper, we can reduce 21% of cross-shard queries by increasing the shard imbalance factor from 0% to 10%. As discussed in our paper, live experiments on real traffic show that the number of multi-shard queries from our cut-optimization techniques is 40% less compared to a baseline Hilbert embedding technique. This, in turn, results in less CPU usage in response to queries. In a future blog post, we will talk about application of this work in the web search serving backend, where balanced partitioning helped us design a cache-aware load balancing system that dramatically reduced our cache miss rate.
We especially thank Vahab Mirrokni whose guidance and technical contribution were instrumental in developing these algorithms and writing this post. We also thank our other co-authors and colleagues for their contributions: Raimondas Kiveris, Soheil Behnezhad, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Silvio Lattanzi, Aaron Archer and other members of NYC Algorithms and Optimization research team.