We had a similar challenge when we managed roads and their quality in Alberta. We were mostly interested in if the roads had degraded, and needed to manage resources to improve them to a level where they would satisfy predicted transport needs. We had Aerial and Satellite images. Now you can selectively deploy drones. And we needed to include predicted changes like tree growth, storms and forest fires. This does some of the things we could have used then. Perhaps also some applications to supply chain needs?
A new way to automatically build road maps from aerial images
“RoadTracer” system from the Computer Science and Artificial Intelligence Laboratory could reduce workload for developers of apps like Google Maps. .... By Adam Conner-Simons | MIT, CSAIL
A New Way to Automatically Build Road Maps From Aerial Images
" ... Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have developed a system for automatically constructing road maps from aerial images with 45-percent more accuracy than existing methods. The RoadTracer system “is well-suited to map areas of the world where maps are frequently out of date, which includes both places with lower population and areas where there's frequent construction," says MIT professor Mohammad Alizadeh. RoadTracer begins with a known location on a road network, using a neural network to examine the surroundings and determine which point is most likely to be the next part of the road. It adds that point and repeats the process to plot out the network of the road step by step..... "
(Updated) See also 'Automated site Mapping' Used in Archaeology, related method beyond examining just roads.
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