Rapid urbanisation brings severe challenges on sustainable development and living quality of urban residents. Smart cities develop holistic solutions in the field of urban ecosystems using collected data from different types of Internet of Things (IoT) sources. Today, smart city research and applications have significantly surged as consequences of IoT and machine learning technological enhancement. As advanced machine learning methods, deep learning techniques provide an effective framework which facilitates data mining and knowledge discovery tasks especially in the area of computer vision and natural language processing. In recent years, researchers from various research fields attempted to apply deep learning technologies into smart city applications in order to establish a new smart city era. Much of the research effort on smart city has been made, for example, intelligence transportation, smart healthcare, public safety, etc. Meanwhile, we still face a lot of challenges as the deep learning techniques are still premature for smart city. In this thesis, we first provide a review of the latest research on the convergence of deep learning and smart city for data processing. The review is conducted from two perspectives: while the technique-oriented view presents the popular and extended deep learning models, the application-oriented view focuses on the representative application domains in smart cities. We then focus on two areas, which are intelligence transportation and social media analysis, to demonstrate how deep learning could be used in real-world applications by addressing some prominent issues, e.g., external knowledge integration, multi-modal knowledge fusion, semi-supervised or unsupervised learning, etc. In intelligent transportation area, an attention-based recurrent neural network is proposed to learn from traffic flow readings and external factors for multi-step prediction. More specifically, the attention mechanism is used to model the dynamic temporal dependencies of traffic flow data and a general fusion component is designed to incorporate the external factors. For the traffic event detection task, a multi-modal Generative Adversarial Network (mmGAN) is designed. The proposed model contains a sensor encoder and a social encoder to learn from both traffic flow sensor data and social media data. Meanwhile, the mmGAN model is extended to a semi-supervised architecture by leveraging generative adversarial training to further learn from unlabelled data. In social media analysis area, three deep neural models are proposed for crisis-related data classification and COVID-19 tweet analysis. We designed an adversarial training method to generate adversarial examples for image and textual social data to improve the robustness of multi-modal learning. As most social media data related to crisis or COVID-19 is not labelled, we then proposed two unsupervised text classification models on the basis of the state-of-the-art BERT model. We used the adversarial domain adaptation technique and the zero-shot learning framework to extract knowledge from a large amount of unlabeled social media data. To demonstrate the effectiveness of our proposed solutions for smart city applications, we have collected a large amount of real-time publicly available traffic sensor data from the California department of transportation and social media data (i.e., traffic, crisis and COVID-19) from Twitter, and built a few datasets for examining prediction or classification performances. The proposed methods successfully addressed the limitations of existing approaches and outperformed the popular baseline methods on these real-world datasets. We hope the work would move the relevant research one step further in creating truly intelligence for smart cities
