Accordingto official statistics for 2018, in the past two decades, China has made remarkable achievement in urbanization process. However, the rapid growth of urbanization brings great challenges to the urban governance. To address the challenges brought about by massive population growth, smart cities have been proposed and continuously developed in practice. This article introduces the urbanization process and its characteristics, analyzes the developments and typical characteristics of smart cities, and reflects on existing challenges. The research shows that the two most prominent Chinese characteristics in the construction of smart cities are people-oriented and in-depth urban informatization.

Artificial intelligence has become a crucial technology for the development of the times. In the practice of emerging engineering education about artificial intelligence, how to enhance students' understanding of artificial intelligence technology, strengthen students' enthusiasm for learning, and improve their research and application innovation ability is a key issue. This study constructs a student-centered STEM learning model that emphasizes innovative applications, so as to promote project-based learning effects for students, reduce learning burnout as well as improve their research and application capabilities. We take the design improvement and experimental result analysis of the intelligent car target tracking system as an example and verifies the effectiveness of the learning model through multi-dimensional evaluation and comparative experiments. Under the same experimental equipment and necessary project functions, we divide students into STEM learning groups and traditional project learning groups to carry out learning activities. This study focused on the learning process of the STEM learning group. The empirical analysis shows that the project completion time and quantitative evaluation of the effects are better than the general learning model.

As WiFi technology becomes more widespread and integrated, precise location tracking within complex indoor environments is gaining significance in contemporary public spaces.Nevertheless, challenges persist in indoor scenarios, including issues such as noise and multi-path effects stemming from the degradation of the Received Signal Strength Indicator (RSSI). Additionally, obstacles causing signal shading contribute to a decrease in the accuracy of indoor location tracking. Within this paper, we introduce an innovative indoor localization algorithm termed Vision Transformer Indoor Localization (VTIL). This algorithmleverages RSSI and Vision-Transformer (ViT) technologies to enhance indoor positioning accuracy. Initially, the RSSI fingerprints undergo normalization by scaling them to the maximum and minimum values. To mitigate the impact of noise and irrelevant features, the Principal Component Analysis (PCA) algorithmis then employed for effective feature extraction.Secondly, the RSSI fingerprint library is converted into an RSSI gray image library. Then the RSSI gray image is divided into several small blocks, and the position-coding input is performed in the form of a sequence. The ViT model divides the weight ratio of each block in the RSSI image to alleviate the impact of multi-path effects. According to the experimental results using public datasets, our approach achieves a noteworthy 37.26% reduction in the average distance estimation error when compared to existing indoor fingerprint localization algorithms.