Mapmaking is a centuries-old tradition. In the past, adventurous trailblazers journeyed into the wild to record landscape features, roads, and settlements.<\/p>\n
Today, most geographic information is collected with technology \u2014 dashcams, drones, and satellites. Thanks to these advances, we can create highly detailed, feature-rich maps and receive real-time navigation instructions.<\/p>\n
And progress in navigation and digital mapping is only picking up speed.<\/p>\n
The location-based services (LBS) market is expected to grow tenfold<\/a> between 2021 and 2031. Much of this growth will come from the mobility sector.<\/p>\n The newest generation of advanced driver assistance systems (ADAS) \u2014 and, soon, fully autonomous driving systems<\/a> \u2014 demand real-time access to accurate data, dynamic routing capabilities, and self-healing maps.<\/p>\n Although mapping and navigation data is easier to procure now than it was ten years ago, gathering it is still labor-intensive. Google Maps development was a multi-year, multi-billion-dollar effort led by a global team of engineers. Most of the road features in the early product version were \u201chand-massaged by a human,\u201d as an article in The Atlantic<\/a> describes it.<\/p>\n Even though most corners in the world today have already been recorded in public and proprietary geographic information systems (GIS)<\/a>, maps still require regular maintenance. Data accuracy and freshness are the two main challenges in the mobility industry, followed by coverage (the physical world keeps evolving too).<\/p>\n \u2026And then there\u2019s the biggest challenge, of course \u2014 delivering dynamic map updates and competitive navigation products.<\/p>\n That said, a good industry challenge is an ample opportunity for new product engineering<\/a>.<\/p>\n Here are the six most interesting innovations in the navigation and mapping space that are driving future competitiveness and market growth.<\/p>\n Satellite imagery was a breakthrough for map creation. The wrinkle, however, is that most mapping software cannot work directly with satellite photos.<\/p>\n Visual data first needs to be codified into comprehensive navigation datasets in a suitable format such as the Navigation Data Standard (NDS). Then map owners must keep it up to date. Both processes are cost- and labor-intensive, making them great use cases for AI in mapping.<\/p>\n AI algorithms improve the speed and precision of digital map building by offering the ability to update maps more regularly and map new areas faster. They can classify objects in satellite images \u2014 buildings, roads, vegetation \u2014 to create enriched 2D digital maps as well as multi-layer 3D map models.<\/p>\n With precise maps, you can delight users with better ETA predictions<\/a>, detailed fuel or energy usage estimates, and richer point-of-interest information. For example, Grab \u2014 Asia\u2019s biggest ride-hailing company \u2014 uses , and proprietary AI models to detect undocumented road segments and update maps promptly. With always up-to-date location insights, Grab improves dispatch management for ride booking and deliveries as well as ETA estimates.<\/p>\n Apart from facilitating the collection of mapping data, AI can also help with generating such data.<\/strong><\/p>\n Researchers from MIT and the Qatar Computing Research Institute (QCRI) recently released RoadTagger<\/a> \u2014 a neural network that can automatically predict the road type (residential or highway) and number of lanes even with visual obstructions present (such as a tree or building).<\/p>\n The model was tested on occluded roads from digital maps of 20 US cities. It correctly predicted the number of lanes with 77% accuracy and predicted road types with 93% accuracy. The team is now training the model to predict other road features such as parking spots and bike lanes.<\/p>\n That said, sensor data collection from connected vehicles isn\u2019t going anywhere. OEMs are increasingly relying on their fleets to collect new insights for digital map creation, and this process is becoming easier with advances in machine learning.<\/p>\nEnriching mapping data with AI<\/h2>\n