Many cities around the world are striving to leverage their resources to modernize the traditional mechanisms of their operation and to acquire dynamic behavior to deal with the urban problems arising from anarchic urbanization and urban aging, and to become a pole of attraction for citizens and investors. City transformation is a complex and multidimensional process as it depends on the collective integration of governance, technology, institutional and transitional components [30]. As [31] pointed out, city transformation is based on four key areas, which are: (i) urban planning, (ii) physical infrastructure, (iii) ICT infrastructure and (iv) smart solutions’ deployment, and aims at cultivating sustainability, smartness and resilience in cities. In the context of city transformation, particular emphasis is placed on modern governance models’ application, the exploitation of disruptive technologies, strengthening of communities and citizen participation, environment and resources’ protection and emergency management [31,32].

The concept of SC, which was launched about 20 years ago, significantly influenced the city managers and paved the way for the transformation of cities to achieve their sustainability [4]. More than 40 definitions and 30 conceptual models were proposed to clarify the term “smart city” that differ from each other due to the different perspectives and approaches developed for its modeling and design. Many SC definitions emphasize the use of ICT to effectively combine resources to make the city more interconnected, smart and viable, while some other sustainability oriented definitions focus on combining soft infrastructure (i.e., people, knowledge, communities, business processes, etc.) and the hard infrastructure (i.e., ICTs, buildings, city facilities, etc.) to provide a viable, efficient and sustainable city [33]. In the latter case, the term SSC is often used instead of the term SC. Reference [34] considers a smart (sustainable) city as an innovative city that exploits ICTs and other means, with the purpose of improving the quality of life, the efficiency of urban services and operation and competitiveness, while ensuring the needs of present and future generations regarding economic, social and environmental aspects. The improvement of the quality of life and the economy, the development of efficient urban infrastructure, ensuring social inclusion, sustainable management and conservation of natural resources and ensuring good governance are the main goals of SC [4]. According to the conceptual model of [35], the SC ecosystem consists of six dimensions, which are: (i) smart economy, (ii) smart governance, (iii) smart environment, (iv) smart people, (v) smart mobility and (vi) smart living.

Along with the establishment of smart city, the concept of resilient city (RC) emerged [36,37]. The term “resilience” came to the fore in 2012, after Hurricane Sandy, which caused a total of about USD 19 billion in damage, and is associated with risk management, ecology and political sciences [36]. In this context, international organizations and city associations promoted the term “resilient city” to improve cities’ capabilities to deal with risks and external pressures, ranging from climate change and environmental degradation to poverty and congestion. As pointed out in [32], RCs are those that have the ability to absorb, recover and prepare for future shocks (economic, environmental, social and institutional) and promote sustainable development, well-being and inclusive growth. The COVID-19 crisis that spread around the world intensified the need to integrate resilience into local government recovery strategies [38]. The achievement of resilience in cities is driven by four interrelated areas, which are: (i) economy, (ii) environment, (iii) governance and (iv) society [32]. Citizen engagement and co-creation are also considered essential to achieving resilience in cities. Regarding this, the authors of Reference [39] proposed the redistribution of power and the redesign of urban services with the purpose of enhancing citizen participation and equality. Taking into account the abovementioned and the relevant literature [37,40], it appears that RCs, through the mechanisms they develop, aim at preventing natural disasters (e.g., floods, earthquakes, hurricanes, etc.), managing emergencies (e.g., health crises, fires, etc.) effectively, civil protection and maintaining social cohesion and economic development.

Although the concepts of SCs and RCs have different roots and missions, they have many similarities, and there is an overlap of their key features, some of which are: efficiency, flexibility, learning and innovation capacity, participation, awareness, etc. The features that are unique to RCs and contribute to their adaptability are the following: persistence, modularity, redundancy, memory, robustness, resourcefulness and transformability. As far as SCs are concerned, their key features are: equity, monitoring capacity, reliability and anticipation [37]. In this respect, the McKinsey Institute [41] pointed out that “smarter cities are resilient cities”, since city monitoring through smart infrastructure leads to the acquisition of profound knowledge and timely decision-making and execution of actions. Studying the differences and similarities between SCs and RCs, the authors of Reference [42] concluded that the impact of RCs is positive on smart cities from a physical, social and environmental point of view, while the impact of SCs on RCs can be both positive and negative from the above three aspects. Additionally, both SCs and RCs are equally important for urban planning and can complement each other through proper planning and governance. Therefore, city managers need to devise transformation strategies that will lead to the realization of both smart and resilient cities, to meet the challenges of rapid urbanization and to achieve sustainable urban development. Since each city has its own needs and characteristics (intrinsic and extrinsic) and there is no highroad to achieve city transformation, the acquisition of profound knowledge that will come from urban data and the use of relevant standards to steer and measure city performance are proving necessary.

Cities act as “data prosumers”, since huge amounts of data are generated and consumed on a daily basis [4]. The main driving force behind the urban data production is human activity, as people live and work in cities and manage them. The authors of Reference [4], conducting a systematic review on SC data analytics, found that the design, development and maintenance of smart services requires the exploitation of data from various urban data sources. Urban data constitutes a valuable asset of cities as its exploitation provides insights into their operation and performance, which are necessary for decision-making and urban planning [31], and as aptly pointed out by [43], “the city of tomorrow is designed using the data of today”. In this context, several conventional cities that strive to transform into smart and resilient ones have recognized the important role of urban data and developed infrastructure for their collection and utilization [44]. However, the efficient use of urban data raises new issues related to: (i) urban data sources, (ii) data ownership and (iii) data storage and processing [44,45,46].

With regard to urban data sources, the majority of urban data comes from organizations, both public and private are included, that have monitoring and data recording systems or conduct surveys. The use of IoT technologies, despite the investments made in recent years, the value of which will exceed 2 trillion US dollars by 2025 [47], remains prohibitive for several cities due to their limited budgets [5,48,49]. Moreover, the recording of human activity that has great potential is almost negligible, since few tools have been developed for data collection by citizens, citizens have not been motivated to participate in the production of urban data that will benefit cities and themselves, while Online Social Networks (OSNs) offer limited opportunities for the exploitation of OSN data [4,44]. Several scholars, attempting to address these limitations, highlighted the dynamics of crowdsourcing (or crowd-sensing), since the majority of urban activities are performed and can be recorded by human capital [50,51,52,53]. Crowdsourcing, which is also known as Internet of People (IoP), constitutes a valuable and low-cost urban data source that can be used either independently or in conjunction with IoT to provide real-time data [54,55]. Citizens, using their personal devices such as smartphones, wearables, etc., act as social sensors, and create and provide valuable and real-time information and social content (or User-Generated Content (UGC)), that is impossible to be derived from other technologies (e.g., IoT, GIS, etc.) [50,52]. UGC, which is produced in three different user-centric ways (i.e., participatory sensing, opportunistic sensing, opportunistic mobile social networks), is also used to verify and validate sensor data [4,53]. According to the authors of [56], who conducted a systematic review on crowdsourcing exploitation in SC, crowdsourcing applications are used in the fields of environment, disaster management, public safety, city innovation, transportation and health, while their feasibility is affected by systems’ characteristics that are cost, duration, scalability, technical support and uncertainty. UGC is often exploited in Intelligent Transport Systems (ITS), in which real-time and valid information on traffic conditions is required [48,57,58,59]. Specifically, the authors of Reference [48] pointed out that crowdsourcing is a key mechanism for enhancing citizen participation and collecting urban data, which is necessary for transportation planning and operations. The authors of Reference [58] proposed a multi-agent system which uses UGC and IoT data to address the problem of Vulnerable Road Users (VRUs), while the authors of [60] presented a mobile crowdsourcing-based system, entitled CrackSense, which detects urban road crack, estimating its damage degree. Beyond ITS, crowdsourcing contributes to the improvement of public services and urban life, while enhancing citizen engagement and co-creation [4,61,62]. Therefore, as [63] pointed out, crowdsourcing should be utilized by cities as it effectively contributes to their transformation by enhancing citizen engagement and the exploitation of untapped data that captures and reveals the vibrancy of cities and enables real-time decision-making. However, issues related to the citizens’ privacy protection, data usage rights, citizens’ incentives to participate and the possible malicious involvement of some citizens in crowdsourcing activities should be carefully considered.

In terms of data ownership, most of the urban data belongs to municipalities and private organizations, where it remains locked and is utilized based on their purposes and interests. As part of open government, several cities, such as London, Boston, New York City, Amsterdam, Copenhagen, Barcelona, etc., set up open data platforms and dashboards to allow urban data to be accessed and used by cities’ stakeholders [26,64]. However, the open data platforms of most cities contain archival data from other public or private organizations that are updated monthly or annually, and usually do not cover the full range of activities in cities, and especially human activity. This results in the provision of limited information that is insufficient for the composition of the urban profile and its evolution over time, and reduces the potential of city managers who need rich and qualitative datasets for knowledge acquisition and policy-making. Cities should invest in the development of digital applications that citizens can use to provide feedback to governments quickly and inexpensively through crowdsourcing, which will be interconnected with open data platforms, to increase their interaction with citizens and to improve the quality and quantity of urban data [61].

Regarding the methods of urban data exploitation, it is common to store it in databases and then retrieve and process it and visualize the results of its analysis. This time-consuming sequence is a brake on instant data visualization and real-time decision-making [4,45,46]. Data interoperability and integration constitutes one of the most difficult problems facing cities, as pointed out by [65]. With the purpose of addressing this issue, the large volumes of urban data generated by IoT devices and crowdsourcing activities need to be harnessed to help city applications make informed decisions on the fly. In this respect, the authors of Reference [45] presented the IES Cities platform, which was designed to streamline the development of urban applications that integrate heterogeneous datasets provided by different entities, such as citizens, municipality, IoT infrastructure and other data sources. In addition, the authors of [65] proposed a conceptual framework that aims to integrate data across the various systems of the city, urban data analytics and creation of value-added services using edge computing, cloud computing, data analytics and semantic integration. Of particular interest is the work of [46], which introduced a framework for a real-time decision support system for response during a crisis or disruption of critical infrastructure based on in-memory database technologies and urban data sources. Specifically, data that include decisions and strategies concerning urban resilience are better to be collected from the database according to current urban status and the type of disruption.

As evidenced from the above, and as the authors of [44] pointed out, the aforementioned three factors that determine both the quality and quantity of acquired urban knowledge that are necessary for the successful city transformation are highly volatile and depend on technological advancements. Cities are called upon to develop mechanisms for effective urban data management and to answer questions about urban data ownership and reliability, urban data protection, the usefulness and purpose of data collection and urban data processing and analytics methods to lay the foundations for achieving their smartness and resilience and for measuring their performance [44]. Moreover, local authorities should work closely with citizens and city stakeholders to prioritize needs, develop low-cost services that are delivered on time and achieve city transformation that accelerates urban smart growth. Special emphasis should be placed on the exploitation of crowdsourcing activities, since it offer many benefits, such as strengthening citizen participation, enhancing the city’s vibrancy, decreasing costs of data production, etc. [56,63]. Consequently, cities need to invest in the development of data infrastructures and define the standards for the collection, utilization and ownership of urban data to ensure their fruitful exploitation and gain comparative advantages over other cities.

This section deals with cities’ standardization, which is necessary to define the guidelines for urban transformation, to make feasible the measurement of cities’ performance and to facilitate decision-making. Initially, the efforts made to define cities’ indicators are discussed and then the focus is on the city standards proposed by ISO.