Future Development Of Smart Cities Research Articles

Smart city development Models: A cross-cultural regional analysis from theory to practice

Based on the multiple definitions and cultural interpretations of smart cities, this study systematically compiled the components, standards, and development modes of smart cities in various regions of the world and proposed a new integration of smart city dimensions and operational framework based on the development process, aiming to provide a theoretical basis and practical orientation for the transition of future development of smart cities and the formulation of planning policies. The conclusions of study are as follows: (1) In the process of building smart cities, countries’ understanding of smart cities is guided by their indigenous cultures, presenting various perceptions of smart city definitions, components, and standards in different geographic regions; (2) Scholars have proposed various dimensions and combinations for the development of smart cities based on their regional cultural, technological conditions, and temporal contexts, aiming to explore how cities can become 'smart'. (3) With the accumulation of experience in the construction of smart cities, the individual-technology-culture (IndiTeCultural) dimension model, which is a combination of individuals, technology, and culture, demonstrates better alignment with the requirements of smart city development around the world. This establishes the foundation for the transition from theory to practice in crafting a future operational framework for smart cities. (4) On the basis of cross-cultural geographical analysis, drawing on the practical experiences of smart city initiatives in various countries and the rating levels of their progress, this study analyses the challenges and opportunities faced by smart city development and presents the problems that need to be solved urgently for more effective mechanisms for smart city development.

Exploring diverse interests of collaborators in smart cities: A topic analysis using LDA and BERT

Smart cities have emerged as a promising solution to the problems associated with urbanization. However, research that holistically considers diverse stakeholders in smart cities is scarce. This study utilizes data from four types of collaborators (academia, public sector, industry, and civil society actors) to identify key topics and suggest research areas for developing smart cities. We used latent Dirichlet allocation and Bidirectional Encoder Representations from Transformers for topic extraction and analysis. The analysis reveals that sustainability and digital platform have received similar levels of interest from academia, industry, and government, whereas governance, resource, and green space are less frequently mentioned than technology-related topics. Hype cycle analysis, which considers public and media expectations, reveals that smart cities experienced rapid growth from 2015 to 2021, but the growth rate has slowed since 2022. This means that a breakthrough improvement in the current situation is required. Accordingly, we propose resolving the unbalanced distribution of topic interests among collaborators, especially in the areas of governance, environment, economy, and healthcare. We expect that our findings will help researchers, policymakers, and industry stakeholders in understanding which topics are underdeveloped in their fields and taking active measures for the future development of smart cities.

A new framework for assessment of park management in smart cities: a study based on social media data and deep learning

Urban park management assessment is critical to park operation and service quality. Traditional assessment methods cannot comprehensively assess park use and environmental conditions. Besides, although social media and big data have shown significant advantages in understanding public behavior or preference and park features or values, there has been little relevant research on park management assessment. This study proposes a deep learning-based framework for assessing urban park intelligent management from macro to micro levels with comment data from social media. By taking seven parks in Wuhan City as the objects, this study quantitatively assesses their overall state and performance in facilities, safety, environment, activities, and services, and reveals their main problems in management. The results demonstrate the impacts of various factors, including park type, season, and specific events such as remodeling and refurbishment, on visitor satisfaction and the characteristics of individual parks and their management. Compared with traditional methods, this framework enables real-time intelligent assessment of park management, which can accurately reflect park use and visitor feedback, and improve park service quality and management efficiency. Overall, this study provides important reference for intelligent park management assessment based on big data and artificial intelligence, which can facilitate the future development of smart cities.

Personalized Ambient Pollution Estimation Based on Stationary-Camera-Taken Images Under Cross-Camera Information Sharing in Smart City

Timely and high-density air quality monitoring is essential for the development of future smart cities. The images captured from widely deployed stationary cameras can be transferred quickly via the Internet of Things (IoT) to facilitate ambient pollution estimation anytime anywhere. Imaged-based air pollution estimation is normally formulated as a supervised learning problem, relying on an extended number of image samples. However, individual stationary cameras can offer only very limited samples and scenes, whilst locally trained estimation models can easily overfit. A global method was proposed to address this challenge. The global model was trained via images captured from different cameras. However, such a model is less effective in extracting local features from scenes. A personalized method is therefore proposed to improve not only the generalization of the estimation model but also to preserve the local characteristics of individual cameras. Our personalized method consists of a two-stage architecture: (1) images from different cameras are used to train the global estimation model to avoid overfitting due to fixed scenes and small sample size; (2) the global model is further refined by images captured from individual cameras separately for adapting local characteristics. To evaluate our proposed personalized method, a large dataset was constructed, based on stationary camera-taken images captured in Hong Kong, consisting of different pollution measurements, including PM2.5, PM10, NO2 and O3. As compared to the local model, our proposed personalized model has reduced average MAE by 5.68% and average SMAPE by 6.82%, and improved average r by 4.69%.

Modular Floating City: The Road to Future Development of Smart Cities

Modular Floating City: The Road to Future Development of Smart Cities

Smart city based on digital twins

Digital twins are considered to be a new starting point for today’s smart city construction. This paper defines the concepts of digital twins and digital twin cities, discusses the relationship between digital twins and smart cities, analyzes the characteristics of smart cities based on digital twins, and focuses on the five main applications of smart cities based on digital twins. Finally, we discuss the future development of smart cities based on digital twins.

The Future of Mapping – 3D Maps, the Comparison of two of the Most Used Methods in Photogrammetric Field

Abstract In the last 15 years, mapping technology has become a necessity in smart cities planning. And 2D are starting to be augmented by 3D maps. 3D Maps are already used in the cartographic field, to create a three-dimensional view of the terrain and buildings. In this paper we address the concept of 3D Maps and we compare two methods to generate such maps. In this study two 3D maps were built, one using photogrammetric 3D stereo-restitution and one using automatic extrusion from a LiDAR point cloud and a set of 2D vector polygons. Upon comparison of these maps, we have concluded that the accuracy of the two maps is very similar and it depends very much on the input data and we have observed that creating a precise 3D map in photogrammetric environment takes much longer than the one built using the LiDAR point cloud. As 3D maps become the future of mapping, there is a continuous need for more accurate and complete field data to be collected and processed. Once more detailed field data becomes available, a clear conclusion on which of the methods provide us with a more accurate 3D map could be drawn. The evolution of 3D mapping is rapidly growing together with the applications developed to use it, especially in surveying and material monitoring. The key to future development of smart cities in based on better designs and infrastructures, and 3D mapping technology is a vital instrument to assist such a development.

Front-End Smart Visual Sensing and Back-End Intelligent Analysis: A Unified Infrastructure for Economizing the Visual System of City Brain

The visual data, which are acquired from the ubiquitous visual sensors deployed in metropolitans, are of great value and paramount significance to enhance the effectiveness and pursue the future development of smart cities. In this paper, the essential building blocks of the unified visual data management and analysis infrastructure that serve as the foundation for the economical visual system in the city brain, are introduced to facilitate the utilization of the visual signal in the artificial intelligence era. In particular, we start by the discussion of the front-end smart visual sensing in the context of economical communication and service with the heterogeneous network, and the functionalities and necessities of compact visual feature and deep learning model representations are detailed. Subsequently, the utilities of the infrastructure are demonstrated through two intelligent applications at the back-end, including vehicle re-identification and person re-identification. The standardizations regarding compact feature and deep neural network representations, which are regarded as the key ingredients in this infrastructure and greatly facilitate the construction of the visual system in the city brain, are also discussed. Finally, we envision how the potential issues regarding the economical visual communications for future smart cities might be pragmatically approached within this unified infrastructure.

Building form regulation visualisation in Japan: automatic building volume legality verification via procedural modelling

Building Standard Law (BSL) in Japan serves to establish minimum standards relating to the site, structure, facility, and function of buildings. Building form regulation (BFR) in BSL controls the building volume according to the zone restrictions contained within a district plan. In Japan, one of the building permission (BP) procedures involves determining whether building volume is legal or not according to BFR. This paper proposes a 3D BFR visualisation which allows for efficient legality verification under BP. We establish shape parameters to transform BFR into a 3D shape reflective of certain land conditions which affect the attributes in CityEngine. We then utilise the 3D BFR shape visualisation to validate the building volume legality automatically in python script. We hope that the results presented here improve the efficiency of the BP procedure. And regulations may be combined into the city data. It is meaningful for the future development of smart cities.

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

The development of pervasive communication devices and the emergence of the Internet of Things (IoT) have acted as an essential part in the feasibility of smart city initiatives. Wireless sensor network (WSN) as a key enabling technology in IoT offers the potential for cities to get smarter. WSNs gained tremendous attention during the recent years because of their rising number of applications that enables remote monitoring and tracking in smart cities. One of the most exciting applications of WSNs in smart cities is detection, monitoring, and tracking which is referred to as object tracking sensor networks (OTSN). The adaptation of OTSN into urban cities brought new exciting challenges for reaching the goal of future smart cities. Such challenges focus primarily on problems related to active monitoring and tracking in smart cities. In this paper, we present the essential characteristics of OTSN, monitoring and tracking application used with the content of smart city. Moreover, we discussed the taxonomy of OTSN along with analysis and comparison. Furthermore, research challenges are investigated concerning energy reservation, object detection, object speed, accuracy in tracking, sensor node collaboration, data aggregation and object recovery position estimation. This review can serve as a benchmark for researchers for future development of smart cities in the context of OTSN. Lastly, we provide future research direction.

Contextual Sensing: Integrating Contextual Information with Human and Technical Geo-Sensor Information for Smart Cities.

In this article we critically discuss the challenge of integrating contextual information, in particular spatiotemporal contextual information, with human and technical sensor information, which we approach from a geospatial perspective. We start by highlighting the significance of context in general and spatiotemporal context in particular and introduce a smart city model of interactions between humans, the environment, and technology, with context at the common interface. We then focus on both the intentional and the unintentional sensing capabilities of today’s technologies and discuss current technological trends that we consider have the ability to enrich human and technical geo-sensor information with contextual detail. The different types of sensors used to collect contextual information are analyzed and sorted into three groups on the basis of names considering frequently used related terms, and characteristic contextual parameters. These three groups, namely technical in situ sensors, technical remote sensors, and human sensors are analyzed and linked to three dimensions involved in sensing (data generation, geographic phenomena, and type of sensing). In contrast to other scientific publications, we found a large number of technologies and applications using in situ and mobile technical sensors within the context of smart cities, and surprisingly limited use of remote sensing approaches. In this article we further provide a critical discussion of possible impacts and influences of both technical and human sensing approaches on society, pointing out that a larger number of sensors, increased fusion of information, and the use of standardized data formats and interfaces will not necessarily result in any improvement in the quality of life of the citizens of a smart city. This article seeks to improve our understanding of technical and human geo-sensing capabilities, and to demonstrate that the use of such sensors can facilitate the integration of different types of contextual information, thus providing an additional, namely the geo-spatial perspective on the future development of smart cities.

Enabling smart cities through a cognitive management framework for the internet of things

The Internet of Things (IoT) is expected to substantially support sustainable development of future smart cities. This article identifies the main issues that may prevent IoT from playing this crucial role, such as the heterogeneity among connected objects and the unreliable nature of associated services. To solve these issues, a cognitive management framework for IoT is proposed, in which dynamically changing real-world objects are represented in a virtualized environment, and where cognition and proximity are used to select the most relevant objects for the purpose of an application in an intelligent and autonomic way. Part of the framework is instantiated in terms of building blocks and demonstrated through a smart city scenario that horizontally spans several application domains. This preliminary proof of concept reveals the high potential that self-reconfigurable IoT can achieve in the context of smart cities.