A mobile SQUID:Street Quality Identification System-based non-distractive evaluation apparatus was realized by the active shielding technique. The system can offer information from beneath the surface of the specimen by using a saw-wave excitation method. This mobile technology enables us to inspect ferromagnetic materials and whose high magnetic field rules out the use of a conventional SQUID apparatus near them. Helping city agencies make better decisions on street maintenance by the creating a low-cost sensor platform. that collects and analyzes data through street surface imagery.The maintenance of city streets is a most of the visible indicator of a city government’s performance.To this end, we develop SQUID, a low cost data platform that integrates open source technologies to combine street imagery. And ride quality data to provide a visual ground truth for all the city’s streets. ARGO has also demonstrated this technology to the Mayor’s Office of  the Operations in NYC. and has discussed implementation in Los Angeles with the City’s Chief Data Officer, Abhi Nemani. This data discovery will enable richer street quality maps and improved street maintenance operations.Fixing potholes today is a game of  whack-a-mole as cities are reactive than proactive about fixingpotholes. SQUID enables a comprehensive map of a city’s street surface quality and transforming the the existing paradigm. In an age of the autonomous vehicle, cities and municipalities need digital tools to ensure that their streets are well maintained at at-cost.To keep track of how well your Squid installation is doing, it’s helpful to analyze the logs that it produces. Numerous log analyzers exist for Squid; some are textonly, others HTML, and others are graphical.To detect the cracks and street quality we can be train different classifiers using computer vision.