2016 ~ Present| Section - VVC

Smart City Infrastructure

GOALS: To develop smart city infrastructure health condition monitoring, detection and diagnosis with the use of emerging technologies (e.g. smart phones, 2D imaging, 3D laser, LiDAR, UAV, GPS/GIS, crowdsourcing, voice recognition, etc.) with artificial intelligence, machine learning, computer vision, pattern recognition, signal processing, and multi-source/scale/frequency/resolution data fusion, and spatial-temporal analyses for innovatively providing smart location-based services.

1)     To identify roadway asset deficiencies, such as potholes, cracking, etc., and dangerous roadway spots/sections that require safety improvement, etc.

2)     To cost-effectively and sustainably preserve and manage the infrastructure assets (pavements, signs, etc.) at the right time, right location, and right method by analysis of multiple sensors at multiple scales.

METHODS & TECHNOLOGIES: Smart phones/tablet PC, cloud computing, parallel computing, image/signal processing, computer vision, machine learning, spatial analysis, non-destructive testing.


1)     Big data analysis, including multi-source/scale/frequency/resolution data fusion and spatial-temporal data analysis for infrastructure health condition monitoring, detection and diagnosis,

2)     Study of fundamental characteristics of different sensing data, including smart phone accelerometer data, 3D laser, mobile LiDAR, GPS/IMU, etc.;

3)     Innovative integration of hardware, algorithms, and procedures to develop innovative solutions (e.g. mobile applications/tools);

4)     Real-world and real-time infrastructure behavior study of its health condition and deterioration behavior in support of smart and resilient cities development.

MEETING TIME: Wed, 9:00 - 9:50

ADVISORS: Dr. James Tsai (CEE), Dr. Tony Yezzi (ECE)                                 

PARTNERS & SPONSORS: United States Department of Transportation (USDOT), National Academies of Science (NAS) National Cooperative Highway Research Program (NCHRP), Georgia Department of Transportation (GDOT), National Science Foundation                                                                 


ECE, CS, ME – Background/interest in mobile computing, Android/iOS development, cloud computing, database development (smart services development)

ECE, CS, ME – Background/interest image processing, computer vision, pattern recognition, GPS/GIS (intelligence service development)

CEE, ISYE – Transportation asset management, pavement management, roadway safety

CONTACT: Dr. Tsai, james.tsai@ce.gatech.edu