Treffer: Leveraging UAV Imagery and Deep Learning for Automated Object Detection.

India is one of the leading countries in rapid global infrastructure development. Road infrastructure is one of the major contributors to the same. This raises a need for the real-time maintenance of the developed infrastructure. In maintenance, precise identification and management of potholes are important, considering the safety of citizens. The current study presents a geo-intelligent framework for real-time detection of potholes. It uses advanced deep learning techniques such as PSP-Net and U-Net for pothole detection. It employs high-resolution unmanned aerial vehicle (UAV) imagery, digital surface model (DSM), along with training samples identified through annotations for model training and evaluation. Experimental results show that U-Net outperforms PSP-Net with an F1-score of 0.78, demonstrating high precision in pothole determination. This novel framework is further deployed in the form of a toolkit in the ESRI ArcGIS ecosystem. The two tools developed using the Python API were deployed for the determination of pothole volume and fill quantity estimation, respectively. The American Concrete Institute (ACI) approach was used to estimate the amount of repair materials needed for the identified potholes. The study helps in the reduction of man-hour efforts needed for lengthy field surveys for pothole identification. The Geo-Image Analytics toolbox offers a scalable solution for evolving urban infrastructure needs, marking a significant step forward in modernizing pothole management practices and the sustainability of the road infrastructure. [ABSTRACT FROM AUTHOR]