Street Level

Street-level imagery analysis leverages computer vision and machine learning to extract valuable information from publicly available and privately held street view datasets. Current research focuses on developing and applying deep learning models, such as convolutional neural networks and attention-based architectures, for tasks including road surface quality assessment, damage detection, and the identification of social activities and urban changes. This research is significant because it provides cost-effective, scalable methods for monitoring urban environments, improving infrastructure management, and informing urban planning and policy decisions, ultimately enhancing safety and livability.