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Rohan Singh Wilkho is a Schmidt AI in Science Postdoctoral Fellow at Cornell University. Holding a PhD in Civil Engineering and an MS in Computer Science from Texas A&M University, he works at the intersection of physical infrastructure and digital intelligence. Rohan develops 'Cognitive Civil Systems', infrastructure designed to perceive and adapt to its environment like a living organism. He applies AI-driven methods to surface critical behavioral and distress patterns in infrastructure networks, ensuring they remain resilient in an unpredictable world.
An example of a Cognitive Civil System is a Road-Lens available at https://road-lens.infraframe.com/
Road-Lens uses an advanced artificial intelligence pipeline to transform standard commercial satellite imagery into highly detailed roadway assessments. This allows cities to monitor their entire road network from space without the prohibitive logistics and costs of physical ground-based inspections. By reconstructing street-level surface details, the platform identifies pavement damage with the same accuracy as a human inspector performing a windshield survey. Ultimately, it generates a continuous digital replica of a city's road network, empowering local governments to shift from reactive, complaint-driven repairs to predictive governance, and democratizing infrastructure.
By Nevena VajdicRohan Singh Wilkho is a Schmidt AI in Science Postdoctoral Fellow at Cornell University. Holding a PhD in Civil Engineering and an MS in Computer Science from Texas A&M University, he works at the intersection of physical infrastructure and digital intelligence. Rohan develops 'Cognitive Civil Systems', infrastructure designed to perceive and adapt to its environment like a living organism. He applies AI-driven methods to surface critical behavioral and distress patterns in infrastructure networks, ensuring they remain resilient in an unpredictable world.
An example of a Cognitive Civil System is a Road-Lens available at https://road-lens.infraframe.com/
Road-Lens uses an advanced artificial intelligence pipeline to transform standard commercial satellite imagery into highly detailed roadway assessments. This allows cities to monitor their entire road network from space without the prohibitive logistics and costs of physical ground-based inspections. By reconstructing street-level surface details, the platform identifies pavement damage with the same accuracy as a human inspector performing a windshield survey. Ultimately, it generates a continuous digital replica of a city's road network, empowering local governments to shift from reactive, complaint-driven repairs to predictive governance, and democratizing infrastructure.