Tag: Uber Maps
Meet a few of the engineers from Uber's Boulder, Colorado office, working on everything from maps to new mobility to large-scale distributed systems.
Uber's Maps Collection and Reporting (MapCARs) team shares best practices when choosing which HDFS file formats are optimal for use with Apache Spark.
In our ongoing series about rewriting the Uber driver app, engineer Chris Haugli explains how we designed the map display to be resilient, and always show the most useful information.
To improve our maps, Uber Engineering analyzes customer support tickets with natural language processing and deep learning to identify and correct inaccurate map data.
Maps make up the bedrock of Uber's transportation solutions. Find out how we ensure the quality of our map data through extensive metrics computation, maintaining fidelity to real world locations and pinpointing allowable pick up and drop off locations for riders.
Uber’s Sensing, Inference, and Research team released a software upgrade for GPS on Android phones that significantly improves location accuracy in urban environments.
In this article, we highlight how Uber leverages machine learning and artificial intelligence to tackle engineering challenges at scale.
In this article, members of Uber Bangalore Engineering discuss their role in building reliable transportation systems at scale for India—and beyond.