AbstractIn the big data era, with the large volume of available data collected by various sensors deployed in urban areas and the recent advances in AI techniques, urban computing has become increasingly important to facilitate the improvement of people’s lives, city operation systems, and the environment. In this chapter, we introduce the challenges, methodologies, and applications of AI techniques for urban computing. We first introduce the background, followed by listing key challenges from the perspective of computer science when AI techniques are applied. Then we briefly introduce the AI techniques that are widely used in urban computing, including supervised learning, semi-supervised learning, unsupervised learning, matrix factorization, graphic models, deep learning, and reinforcement learning. With the recent advances of deep-learning techniques, models such as CNN and RNN have shown significant performance gains in many applications. Thus, we briefly introduce the deep-learning models that are widely used in various urban-computing tasks. Finally, we discuss the applications of urban computing including urban planning, urban transportation, location-based social networks (LBSNs), urban safety and security, and urban-environment monitoring. For each application, we summarize major research challenges and review previous work that uses AI techniques to address them.
A deep learning-based surface defect inspection system using multi-scale and channel-compressed features
