Image-driven smart urban sensing (ISUS) has emerged as a powerful sensing paradigm to capture abundant visual information about the urban environment for intelligent city monitoring, planning, and management. In this paper, we focus on a Classification and Super-resolution Coupling (CSC) problem in ISUS applications, where the goal is to explore the interdependence between two critical tasks (i.e., classification and super-resolution) to concurrently boost the Quality of Service (QoS) of both tasks. Previous efforts often focus on solving the two tasks individually and ignore the opportunity to explore the interdependence between them. In this paper, we develop SuperClass, a deep duo-task learning framework, to effectively integrate the classification and super-resolution tasks into a holistic network design that jointly optimizes the QoS of both tasks. The evaluation results on a real-world ISUS application show that SuperClass consistently outperforms state-of-the-art baselines by simultaneously achieving better land usage classification accuracy and higher reconstructed image quality.