Beyond the Map: Solving problems and powering location-based services with imagery
Editor’s Note: Product director Ethan Russell brings us the second installment of our Beyond the Map series. In today’s post, he’ll explain how we use imagery to overcome different mapping challenges around the world to help power businesses with location-based data and insights. For a look at how we use imagery to build our consumer Maps products, tune into the Google Keyword blog soon.
So far in this series we’ve explained, at a high level, how we combine imagery, third-party authoritative data, machine learning, and community contributions to continuously map the changing world. But what do we do when one of these key elements is missing, like authoritative data sources? Or when a city is growing so fast that traditional map making isn’t an option? Or when streets are so narrow, we can’t drive a Street View car through to map them? We run into endless mapping challenges in our tireless pursuit to map the world, but the one constant is that imagery is almost always the foundation of the solution.
Mapping growing cities from imagery
Some areas of the world simply don’t have basic roads and buildings mapped yet, which means we can’t reference basic mapping information from authoritative data sources like local governments and organizations. In these cases we build the map literally from the ground up, starting with imagery from which we can extract mapping data. There are broadly two kinds of imagery that we use. Overhead imagery from satellites and airplanes shows roads and buildings, while street-level imagery lets us see road names, road signs, building numbers and business names. In last month’s post, we touched on how we use machine learning to automatically extract information from imagery and keep maps data up to date for our customers. Let’s take a look at how this served as the foundation for significant improvements of our maps of Lagos, Nigeria and what that means for a local business using Google Maps Platform.
Once we had the necessary imagery of the area, we were able to use a number of our machine learning-based pipelines to quickly update the major components of the map within just a few months (traditional mapping processes can often take far longer). We focused on three deep-learning based approaches: drawing the outlines of buildings, identifying house numbers, and recognizing businesses. We created detailed outlines of buildings using a model trained not only on the per-pixel details of what constitutes a building, but also on the high-level traits of building shapes seen in the overhead imagery. To identify house numbers and businesses, we used three-part detection, classification, and extraction approaches based on the continuation of work discussed in this paper. These two algorithms were fed high-resolution Street View imagery as input. The accurate positioning of these images in six degrees of freedom was critical to getting the position of the house or business exactly right. As a result, we were able to improve the quality of our map data in Lagos in about one year (from 2017 to 2018) to levels equivalent to countries where we’ve spent many years building the maps.
For many people, an incorrect address when trying to find a business or other location is just a small nuisance. But for businesses, it could mean loss of business. And for Lifebank, a company that connects blood suppliers to hospital patients in Lagos, it could be a matter of life and death. In 2016, founder Temie Giwa-Tubosun, used Google Maps Platform to create and map an online blood repository in partnership with 52 blood banks across Lagos allowing doctors to request a blood type and immediately access a map that tracks the journey of the delivery.
Before LifeBank, finding and delivering blood to a patient in Lagos could take several hours and in some cases, several days. But LifeBank changed that by transporting blood in an average of 45 minutes from initial request to final delivery. The team has registered over 5,800 blood donors, moved over 13,800 pints of blood, served 300-plus hospitals, and saved more than 3,600 lives. For Temie, access to mapping information was an important part of solving the blood crisis problem in her native Nigeria.
Mapping narrow roads with Street View 3-wheelers
Places like Indonesia have some roads that are too narrow for cars, but just right for the 2-wheelers that are commonly used in the country. We needed to map these roads in order to introduce 2-wheeler navigation in Google Maps and provide 2-wheeler navigation solutions to our ridesharing customers, but our Street View cars were too big. Instead, we mounted a Trekker to a 3-wheeler–taking into account both operator safety and local regulations in our vehicle choice–and started mapping the narrow streets.
The solution makes mapping projects in places off the beaten path or areas that might be inaccessible to cars possible and scalable. It enabled us to capture the street-level imagery of narrow roads needed to launch 2-wheeler navigation in Indonesia and improve our maps of the area. Since we’ve launched in Indonesia, we’ve brought 2-wheeler navigation to 21 other countries.
As you can see, imagery really is the foundation for our maps and solving map making problems worldwide. But this is just a look at a couple of the challenges we’ve solved with imagery. It’s an incredible resource for learning about the world and we have lots of creative ways of collecting and using imagery to help people explore and help businesses to build and expand their services–even in hard to map areas. Come back to the Google Maps Platform blog next time for another installment of Beyond the Map. Until then, to learn more about Google Maps Platform, visit our website.