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Thursday, January 23, 2020

AI Enhancing Maps and Goal Process

You could add to this the ability to add contextual process knowledge to a map.   Beyond just what is there, and how do I navigate to X.   Say in the goal context of maintaining a road,  I may want to be shown places where proactive analysis needs to be done, what equipment is needed, what would the cost would be,  how does that fit into budgets and schedules.   That defines a process,  some algorithms, AI pattern recognition to be involved.   Leading to a change in some process plan.   A kind of 'task and process navigation'  to achieve a management process.

Using artificial intelligence to enrich digital maps
Model tags road features based on satellite images, to improve GPS navigation in places with limited map data.

Rob Matheson | MIT News Office

A model invented by researchers at MIT and Qatar Computing Research Institute (QCRI) that uses satellite imagery to tag road features in digital maps could help improve GPS navigation.  

Showing drivers more details about their routes can often help them navigate in unfamiliar locations. Lane counts, for instance, can enable a GPS system to warn drivers of diverging or merging lanes. Incorporating information about parking spots can help drivers plan ahead, while mapping bicycle lanes can help cyclists negotiate busy city streets. Providing updated information on road conditions can also improve planning for disaster relief.

But creating detailed maps is an expensive, time-consuming process done mostly by big companies, such as Google, which sends vehicles around with cameras strapped to their hoods to capture video and images of an area’s roads. Combining that with other data can create accurate, up-to-date maps. Because this process is expensive, however, some parts of the world are ignored.

A solution is to unleash machine-learning models on satellite images — which are easier to obtain and updated fairly regularly — to automatically tag road features. But roads can be occluded by, say, trees and buildings, making it a challenging task. In a paper being presented at the Association for the Advancement of Artificial Intelligence conference, the MIT and QCRI researchers describe “RoadTagger,” which uses a combination of neural network architectures to automatically predict the number of lanes and road types (residential or highway) behind obstructions.  .... "

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