Efficient and current roadway geometry data collection is a critical task for transportation agencies to undertake effective road planning, maintenance, design, and rehabilitation efforts. The methods for gathering such data can be broadly classified into two categories: a) land-based methods, which encompass field inventory, mobile mapping, and image logging, and b) aerial-based methods, which involve satellite imagery, drones, and laser scanning. However, employing land-based techniques for extensive highway networks covering thousands of miles proves arduous and costly, and poses safety risks for crew members. Consequently, there exists a pressing need to develop more efficient methodologies for acquiring this data promptly, safely, and economically. Fortunately, with the increasing availability of high-resolution images and recent strides in computer vision and object detection technologies, automated extraction of roadway geometry features has become feasible.

Or maybe you're wondering why we should even care about trains and how they operate--what is this, the 1800s?--so let's back up a bit. If you think America is solely dependent on trucks to move freight, you might be suffering from tunnel vision: Trains account for a third of the ton-miles--that is, a ton of weight carried a mile--that freight travels in the U.S. every year. That's almost as much as is carried by trucks. The U.S. has the most extensive rail network of any country on earth by miles of track--yes, even bigger than China's--and it's currently facing some of the same snarls and congestion as seemingly every other part of the country's supply chains, on account of unprecedented activity at ports and record demand at some rail hubs. Trains might seem like a mature technology with little room for improvement or expansion, since adding new rail lines is prohibitively expensive, as battles over the cost of the expansion of Amtrak service have shown.