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"People want an authority to tell them how to value things. But they chosethis authority not based on facts or results. They chose it because it seemsauthoritative and familiar." - The Big Short  The pavement condition index is one such a familiar measure used by many UScities to measure street quality and justify billions of dollars spent everyyear on street repair. These billion-dollar decisions are based on evaluationcriteria that are subjective and not representative. In this paper, we buildupon our initial submission to D4GX 2015 that approaches this problem ofinformation asymmetry in municipal decision-making.  We describe a process to identify street-defects using computer visiontechniques on data collected using the Street Quality Identification Device(SQUID). A User Interface to host a large quantity of image data towardsdigitizing the street inspection process and enabling actionable intelligencefor a core public service is also described. This approach of combining device,data and decision-making around street repair enables cities make targeteddecisions about street repair and could lead to an anticipatory response whichcan result in significant cost savings. Lastly, we share lessons learnt fromthe deployment of SQUID in the city of Syracuse, NY.

Digitizing Municipal Street Inspections Using Computer Vision