Deep learning for optimizing urban governance by "sensing-processing-responding" cycle: Recent advances, future prospects and challenges

• Define and propose the technological framework for the Urban Governance Model. • Explore the baselines of deep learning structure from CNNs to Transformers. • The Sensing–Processing–Response framework has aided Urban Governance effectively. • Demonstrate the potential of applying multimodal large models and multi-agent systems. • Weather and video generation technologies help urban governance via shared prediction traits. With accelerating urbanization, traditional governance models are increasingly strained. Deep learning (DL) offers powerful solutions, but its application in urban governance lacks a systematic framework and faces significant hurdles. This paper addresses these gaps through a systematic review of 329 articles published from 2016 to 2025. We introduce a novel Sensing-Processing-Responding framework to classify the technological pathways of DL in urban governance. This framework organizes applications into three core stages: (1) Sensing technologies (e.g., CNNs) for dynamic data acquisition; (2) Processing technologies (e.g., RNNs, Transformers) for predictive modeling and analysis; and (3) Responding technologies (e.g., LLMs) for automated decision support. Our analysis reveals that while DL is widely applied in traffic forecasting, environmental monitoring, and disaster response, its deployment is constrained by key challenges. We found a significant gap between research and practice, with only 7.6% of studies demonstrating real-world application. Furthermore, it concerns data privacy and model interpretability limit public acceptance, although our review indicates that fewer than 10% of studies involve high-risk personal data. Future progress depends on integrating emerging technologies like multimodal large models and multi-agent systems. We conclude by advocating for a paradigm shift from focusing purely on accuracy to prioritizing public value, fairness, and transparency. This study provides a comprehensive roadmap for developing more intelligent, resilient, and sustainable urban governance systems.