Smart City Development Research Articles

Multi-class Object Detection in Urban Scenes Based on Deep Learning

Abstract. The rapid development of urbanization presents challenges and requirements for multi-class object detection in urban scenes. Accurately identifying buildings, vehicles, and trees in urban scenes can optimize urban planning, traffic management, monitoring environmental conditions, and promote the development of smart cities. Traditional target detection methods perform poorly in complex urban environments, while deep learning technology achieves accurate target recognition and positioning by automatically extracting high-level semantic features. In this study, we chose to use the YOLOv5s algorithm for multi-class target detection in urban scenes. YOLOv5s is a lightweight deep learning model with small storage space and efficient detection speed. In this paper, the Potsdam area data published by ISPRS is used to make the label data of buildings, vehicles and trees. The YOLOv5s algorithm is used to iteratively train the model. The results show that the mAP value detected by the YOLOv5s model can reach 82.83%. The experimental results show that the algorithm shows higher accuracy than SSD and Faster R-CNN in tree detection. Although it has a slight decline in building and vehicle detection, considering the factors such as detection accuracy, speed, and model size, the YOLOv5s algorithm has a better recognition and detection effect for the detection of multi-class targets in urban scenes.

Evaluating the Smart City Status and Acceptance of ISO Standards: A Case Study of Malaysian Local Authority

While most national smart city frameworks outline the urban challenges, local authorities struggle to translate the top-down plan and align the action plan with ISO 37122 indicators, including the case of Majlis Bandaraya Diraja Klang (MBDK), Malaysia. This study aims to evaluate MBDK’s smart city status and examines the acceptance of ISO standards. The quantitative survey findings indicate a positive perception of smart city status, with unanimous agreement on the importance of ISO standards. This research contributed to one of the pioneer studies evaluating the acceptance of ISO indicators in smart city development in Malaysia and beyond.

사회적 자본을 중심으로 찾아본 스마트도시 리빙랩의 성공 요인 분석

In the era of the Fourth Industrial Revolution, modern society is evolving beyond an information society into a hyperconnected society, with the advancement of technology leading to the development of smart cities. According to Cohen (2015), the direction of smart city evolution is shifting from a technology-driven approach to a citizen co-creation model, and living labs play a crucial role in this transition. The objective of this study is to empirically analyze the success factors of living labs in smart cities using the citizen co-creation approach, focusing on social capital. Social capital is measured by variables such as interest, networks, trust, and norms, while the success factors of living labs are analyzed through variables like communication, democracy (conflict acceptance), and open innovation. The analysis results showed that, among the success factors of living labs, communication was positively influenced by trust, norms, and networks, with trust having the greatest impact. Democracy (conflict acceptance) was positively influenced by norms and interest, while open innovation was positively influenced by norms and interest as well. This study concludes that the formation of social capital is crucial for the success of living labs in smart cities following the citizen co-creation model. In particular, the formation of trust among neighbors, colleagues, national and local governments is vital. Additionally, adherence to laws, regulations, and community agreements, as well as interest in various local events and policies, are essential. Based on these findings, this study suggests that while smart cities may have originated from technology, they must ultimately be pursued based on people-centered participation, interest, and trust for sustainable development.

Public Participation in Rapid Response Services for the Sustainability of Jakarta Smart City

Public participation plays an important role in the development of rapid response services to support the sustainability of Jakarta Smart City. This study aims to explore and analyse the level of public participation in utilising and developing these services, and their impact on urban sustainability. The research method used is a qualitative case study through in-depth interviews and document analysis. The results of the study indicate that the level of public participation in rapid response services in Jakarta Smart City varies greatly. Factors such as accessibility, trust, and awareness are the main determinants of the level of public participation. Citizens who actively participate tend to feel greater benefits from these services, while those who are inactive tend to experience gaps in services. Public participation also has a positive effect on service efficiency and the quality of life of city residents. However, this study also found several obstacles that hinder public participation. Low awareness of the importance of the role of individuals in developing smart cities, limited accessibility, and low digital literacy are the main challenges. To increase sustainable public participation, efforts are needed that involve education, training, and strengthening of information technology infrastructure. In conclusion, public participation has a crucial role in the sustainability of Jakarta Smart City through rapid response services. Effective management of public participation can improve service efficiency, strengthen community engagement, and support sustainable urban development. Therefore, continuous efforts are needed to improve and manage public participation for long-term success. With education, training, and improved technological infrastructure, broader and more equitable participation can be realised, supporting the growth of inclusive and sustainable smart cities in the future.

Smart Cities and Human Development: Analysis Using Fuzzy Similarity Measures

Smart Cities and Human Development: Analysis Using Fuzzy Similarity Measures

The Dynamics of the Smart City Development: From the First Wave Model to the Third Wave Model

The Dynamics of the Smart City Development: From the First Wave Model to the Third Wave Model

Exploring best practices in smart public library services: a survey

Purpose This paper aims to the integration of smart cities and technologies has had a profound impact on public library services globally. However, debates continue about how public libraries are incorporating smart technologies and tools, and what this means for their role in smart city development. This paper explores how public libraries are evolving to meet community needs by providing both physical and digital services. Design/methodology/approach The purpose of this study is to assess the role of public libraries in the smart cities and how they are aligning toward development of smart concepts, technologies, services such as contributions to smart city objectives, community impact, the digital divide, unique projects and readiness for future smart city advancements. So, the authors explored 11 public library systems of cities across the world implementing smart city projects. Findings The authors found that all the library systems offer extensive print and digital collections, sustainable buildings and a well-connected network of branches. They provide self-checkout services, makerspaces equipped with 3D printing and coding workshops and advanced recording and video editing technologies. Furthermore, these libraries function as community centers, serving diverse groups including children, women, the elderly and marginalized communities. They function as third spaces that foster lifelong learning and digital literacy. Originality/value Although smart cities face substantial debate regarding their features, tools and feasibility, public libraries have adapted and redefined their roles to become influential public institutions. They now play a crucial part in supporting vibrant and dynamic communities and neighborhoods within smart cities.

High‐Accuracy Liquid Flow Monitoring via Triboelectric Nanogenerator Combined with Bionic Design and Common‐Mode Interference Suppression

AbstractIn the development of smart cities, accurate liquid flow monitoring is essential for the efficient operation of water supply systems. Current flow sensors often face limitations in sensitivity and environmental adaptability, affecting measurement accuracy, and restricting their application in smart city infrastructure. To address these challenges, this study proposes a high‐accuracy flow monitoring method. Specifically, by combining the bionic design with advanced signal processing techniques, the sensitivity and anti‐interference ability are improved, respectively, to enhance the measurement accuracy. Based on this method, a self‐powered flow sensor (SPFS) is developed using noncontact triboelectric nanogenerators (NC‐TENGs) as the sensing unit. The SPFS achieves a sensitivity of 2.07 Hz L−1 min−1 and improves the signal‐to‐noise ratio by more than 13 times over the initial sensing signal. In addition, an intelligent system is developed to accurately measure water resources. The maximum flow rate error rate is less than 0.97% compared to commercial flow sensors. The SPFS demonstrates higher sensitivity and accuracy compared to the existing TENG flow sensors. This study addresses the limitations of existing flow sensors and pioneers a novel solution for enhanced water resource management in smart cities.

How Does the Intelligent Transportation System Promote the Globalization and Digitalization of Cities: Take Tesla Autopilot System as an Example

The globalization and digitization of smart transportation and cities are facilitated by advancements like Teslas autonomous driving technology, which revolutionizes urban mobility. This article explores their interconnection through the example of Teslas system. Teslas Autopilot, based on deep neural networks, utilizes cameras and ultrasonic sensors to perceive the world around vehicles, encouraging data sharing between vehicles and driving global development of smart cities. It employs artificial intelligence to assess terrain and conditions, fostering a transportation revolution in traditional cities and advancing the evolution of smart cities. Moreover, Teslas digital impact is altering transportation solutions, increasing visibility in development, and driving global technological progress. Teslas unique autonomous driving technology, leveraging data from nearly one million Tesla vehicles worldwide, enhances global intelligent transportation systems, promoting global sharing among Tesla users, and facilitating the convenience of autonomous driving and the flow of global traffic information. Teslas autonomous driving mode and intelligent traffic system promote the development of smart cities, although a comprehensive analysis of its global political, economic, and legislative impacts is needed.

Incorporation of Artificial Intelligence in Enhancing Quality of Life in Smart Cities

Rapid urbanization and low residential resources in cities are serious issues that are making city life difficult day by day. The development of smart cities is becoming a need of the present era due to the swift increase in population and environmental issues globally. Smart cities are being introduced in different regions of the world with the incorporation of latest technologies. The incorporation of Artificial Intelligence (AI) is one of the tools that can be used in smart building and cities. AI technologies are transforming public safety, trash management, healthcare, traffic control, and resource management, making cities more sustainable, effective, and responsive to their citizens' demands. There are still lack of awareness in some areas of the world on the efficacy of smart building and construction that is impacting negatively on the economy and growth of those countries.; such as Pakistan is one of those countries that is facing serious challenges due to increased population, urban migration, and poor management of natural resources. The need of planning smart strategies for smart building is very crucial to manage population and housing issues. Smart buildings and cities provide unique and convenient facilities to its residents so that they can contribute positively towards the economy of country. This paper focuses at important areas where AI has the most effects in order to investigate how integrating AI improves quality of life in smart cities. The aim is to highlight artificial intelligence's contribution to improving urban operations, streamlining resource management, and advancing sustainability. Additionally, potential concerns about privacy, data security, and fair access will be discussed. In order to show how AI-driven innovations like predictive analytics, machine learning, and IoT-enabled systems are changing the urban environment, the study synthesizes existing research and real-world examples. The evaluation also covers how AI promotes smart government, tailored urban services, and citizen involvement. The conclusion emphasizes that although AI has great potential to improve the quality of life in smart cities, implementation of the technology must be done in a balanced way to prioritize inclusive policies and ethical concerns for the general welfare of residents.

Research status and development direction of smart heritage: a bibliometric review (1994-2024)

ABSTRACT Smart heritage, an emerging and sustainable paradigm, utilises technology to conserve and enhance cultural heritage. Despite integration into smart city development, full implementation remains unrealised. Consequently, this study aims to investigate the status and trends of smart heritage research, explore the development direction of smart heritage, bridge existing knowledge gaps, and guide its integration into smart city development. This study employs a bibliometric analysis method, supported by machine algorithms, to obtain scientific and quantifiable results. The study utilises the bibliometric analysis software CiteSpace 6.2R6 to analyze 981 articles from the Web of Science Core Collection, covering 1994 to 2024. We observe a notable increase in scholarly output over time, especially in smart cities and digital cultural strategies, where smart heritage has emerged as a research hotspot across various disciplines and domains. Smart heritage crosses three stages: digital technology application, technology integration, and multidisciplinary integration, showcasing diverse technological applications, service scenarios, and application domains. Moving forward, we must prioritize a comprehensive research agenda, including theoretical constructs, smart cultural infrastructure, robust policy, public engagement, and expanded application realms. Additionally, fostering interdisciplinary collaboration between international entities and driving technological innovation is vital for promoting the sustainable development of cultural heritage.

Green Campus Transformation in Smart City Development: A Study on Low-Carbon and Energy-Saving Design for the Renovation of School Buildings

In the context of increasingly deteriorating global ecological conditions and rising carbon emissions from buildings, campus architecture, as the primary environment for youth learning and living, plays a crucial role in low-carbon energy-efficient design, and green environments. This paper takes the case of Yezhai Middle School in Qianshan, Anhui Province, to explore wind environment optimization and facade energy-saving strategies for mountainous campus buildings under existing building stock renovation. In the context of smart city development, integrating advanced technologies and sustainable practices into public infrastructure has become a key objective. Through wind environment simulations and facade energy retrofitting, this study reveals nonlinear increases in wind speed with building height and significant effects of ground roughness on wind speed variations. Adopting EPS panels and insulation layers in facade energy retrofitting reduces energy consumption for winter heating and summer cooling. The renovated facade effectively prevents cold air intrusion and reduces external heat gain, achieving approximately 24% energy savings. This research provides a scientific basis and practical experience for low-carbon energy retrofitting of other campus and public buildings, advancing the construction industry towards green and low-carbon development goals within the framework of smart city initiatives.

Integrating Web-Based Weather Data into Building Information Modeling Models through Robot Process Automation

The rapid evolution of digital technologies has revolutionized the architecture, engineering, and construction (AEC) industry, driving the wide-spread adoption of digital twins for structures. These virtual replicas, developed using Building Information Modeling (BIM) methodology, incorporate extensive information databases, proving indispensable for enhancing project management throughout a structure’s entire lifecycle and towards smart city development. As the impact of climate change continues to grow, hazardous weather alerts play a critical role as an early-warning system that notifies stakeholders of imminent threats, thereby influencing decision-making processes in construction projects. Surprisingly, despite its evident value, the integration of alert systems for hazardous weather conditions into BIM is often overlooked. To fill this gap, this paper proposes Robot Process Automation (RPA) protocols to automate the integration of real-time weather parameters into a structure’s BIM models. These very protocols are also used as alert systems, enabling the timely notification of stakeholders in the event of detected hazardous weather conditions. The effectiveness of the proposed methodology is demonstrated through its practical application in enhancing the safety of an actual building in Viña del Mar, Chile.

STAFGCN:A spatial-temporal attention-based fusion graph convolution network for pedestrian trajectory prediction

Pedestrian trajectory prediction provides crucial data support for the development of smart cities. Existing pedestrian trajectory prediction methods often overlook the different types of pedestrian interactions and the micro-level spatial-temporal relationships when handling the interaction information in spatial dimension and temporal dimension. The model employs a spatial-temporal attention-based fusion graph convolutional framework to predict future pedestrian trajectories. For the different types of local and global relationships between pedestrians, it first employs spatial-temporal attention mechanisms to capture dependencies in pedestrian sequence data, obtaining the social interactions of pedestrians in spatial contexts and the movement trends of pedestrians over time. Subsequently, a fusion graph convolutional module merges the temporal weight matrix and the spatial weight matrix into a spatial-temporal fusion feature map. Finally, a decoder section utilizes TimeStacked Convolutional Neural Networks to predict future trajectories. The final validation on the ETH and UCY datasets yielded experimental results with an Average Displacement Error(ADE) of 0.34 and an Final Displacement Error(FDE) of 0.55. The visualization results further demonstrated the rationality of the model.

Low Network Power Challenges in IoT-Based Applications in Smart Cities

The emergence of Internet of Things (IoT) technologies has led to the development of smart cities, potentially improving urban life through efficient and effective use of resources. The success of IoT-based applications in smart cities is contingent on several factors, including network power challenges. Deploying IoT-based applications in smart cities requires a reliable and efficient network infrastructure to ensure smooth and uninterrupted data communication. One of the significant challenges faced in this regard is low network power, which can significantly impact the performance of IoT devices and, consequently, the entire smart city system. This article addresses the issue of low network power in IoT-based applications in smart cities. It investigates the causes of this problem and the potential solutions that can be adopted to mitigate its impact. The article also highlights the importance of network power management in the context of IoT-based applications in smart cities. By implementing effective network power management techniques, it is possible to optimize the performance of IoT devices and extend their battery life, thus ensuring the overall efficiency and sustainability of the smart city system. The article concludes by emphasizing the need for continuous research and development to overcome the challenges of low network power and further enhance IoT technology's capabilities in smart cities.

Fuzzy based Decision-Making Algorithm for Solving Big Data Issues in Smart Cities

To better provide urban services and build an increasingly sustainable architecture, big data can be used to make more efficient use of current assets while enhancing the caliber of services offered to local inhabitants. However, there are several challenges to incorporating big data into existing infrastructure. Therefore, this research aims to determine the problems associated with Big Data’s effectiveness in developing intelligent towns and to investigate the connections between those difficulties. The 14 issues with Big Data were found through a literature study, and the precision was checked by feedback from professionals. Next, we employ a combined approach based on fuzzy interpretation, Structured simulation, and the Fuzzy Making Decisions Trial and Assessment Laboratories to decipher the connections between our identified problems incorporating Big Data into the development of smart cities is hampered, as shown by the analysis of links between challenges, primarily by the heterogeneous inhabitants in developed cities and the lack of connectivity. The findings of this study will provide creative city practitioners and policy planners with the information they need to successfully tackle these obstacles, clearing the way for the widespread adoption of smart city technologies. This research is a first step towards creating an interpretive structural model of the difficulties brought on by Big Data in cutting-edge urban planning. The study attempts, in part, to use this paradigm to better understand the relationship among the highlighted issues.

Enhancing traffic flow and congestion management in smart cities utilizing SVM-based linear regression approach

With the development of smart cities, it is essential to monitor traffic flow and manage congestion effectively to ensure smooth movement for people and address their social and economic needs. As these needs continue to change, roadside infrastructure faces challenges in meeting the demands of citizens in smart cities. Traffic congestion is a major issue in road networks and occurs when the number of vehicles exceeds the capacity of the roads. Emerging technologies like Vehicular Networks (VN) and Support Vector Machine (SVM)-based linear regression offer promising solutions for vehicle-to-vehicle communication and managing autonomous roadside infrastructure. SVM-based linear regression is a well-known and effective method for addressing various issues related to roadside infrastructure, traffic management, data integration, analytics, and environmental monitoring. The main goal of using SVM-based linear regression in this research is to help citizens and city authorities make informed decisions and better understand and control traffic. This study demonstrates the application of SVM-based linear regression in integrating autonomous roadside infrastructure, achieving a high accuracy rate of 92% and reducing errors by 8%, showing a notable improvement compared to previous methods.

SPATIAL EVOLUTION OF SMART CITIES FOR SUSTAINABLE TOURISM: A CASE STUDY OF PHUKET PROVINCE, THAILAND

In this research, the objective is to study the Spatial Evolution of Smart Cities for Sustainable Tourism in Phuket Province, Thailand from 2005-2024, using the Geo-informatic Technique as information for decision-making in determining the direction of Smart City. The study results found that land use patterns such as Urban and built-up land, Waterbodies, and Forest land have had an increasing trend over the past 20 years. In 2005, Urban and built-up land was found to cover 132.19 km2 (24.10% of the total area). In 2024, it was found to have greatly increased, covering 165.91 km2 (30.25% of the total area), especially in Thalang District, which is north of Phuket. The area has a wide plain landscape, and beautiful wide beaches such as Bang Tao Beach. There has been a change from agricultural land to housing developments, residential areas, restaurants, hotels, resorts and homestays. The results of this study have been prepared as a spatial database for supporting decision-making in monitoring and planning the development of areas to support the expansion of sustainable tourism in the future to become a smart city.

The impact of smart city construction on achieving peak carbon neutrality: Evidence from 31 provinces in China

Amidst the intensification of global warming concerns, addressing climate change has become an urgent imperative, with carbon dioxide emissions standing as the primary driver of the greenhouse effect. This study delves into the relationship between smart city development and carbon emissions in China, leveraging panel data spanning 2012–2021 across 31 provincial units for empirical analysis. The study posits a negative correlation between smart city construction and carbon emissions. To comprehensively gauge the level of smart city development, a weighted evaluation index was constructed using the panel entropy weighting-topsis method. Employing fixed effects, random effects, high-dimensional fixed effects, and two-step system GMM models, the study aims to comprehensively assess the impact of smart city construction levels on carbon emissions. Results reveal that heightened smart city construction levels coincide with reduced carbon emissions, thus substantiating the research hypothesis.

AI and Human-Centric Approach in Smart Cities Management: Case Studies from Silesian and Lesser Poland Voivodships

The presented paper examines the integration of Artificial Intelligence (AI) in the management of smart cities, focusing on the Silesian and Lesser Poland Voivodships in Poland. This research addresses a notable gap in the analysis of regional AI strategies within urban management, providing a comparative analysis of AI implementation in these two distinct regions. The Silesian Voivodship, with its emphasis on traditional industries such as manufacturing and energy, contrasts with the broader approach of the Lesser Poland Voivodship, which includes applications in life sciences and ICT. The paper explores how AI technologies enhance urban efficiency, sustainability, and livability through practical applications in traffic management, healthcare, energy efficiency, and environmental management. It highlights the importance of a human-centric approach in smart city development, emphasizing inclusivity, transparency, and ethical considerations. The paper also delves into the socio-technical dynamics of AI deployment, illustrating how these technologies can transform urban environments while ensuring that the benefits are equitably distributed and that urban developments are sustainable and resilient. By analyzing specific case studies, the authors aim to provide empirical evidence and insights that contribute to the academic and practical understanding of AI’s role in smart cities, ultimately advocating for the design of AI applications that prioritize human well-being and environmental health.