**1. Introduction**

From the practical perspective, a smart city has the capability to capture real-time data that are communicated among stakeholders for optimizing decision-making by deploying artificial intelligence. This is achievable by making activities, services, and businesses smart, e.g., smart real estate, smart transportation, smart construction, smart healthcare system, smart building, smart home, smart transportation, and smart parking. For example, Virtual Singapore [1] is a dynamic 3D city model with a collaborative platform and data sharing system. This virtual city was initiated and funded by the National Research Foundation (NRF) with a \$73 million investment.

Over the past year, the number of mobile users has increased by over two percent, up to 5.11 billion globally [2]. The number of internet users is also increasing. Due to the current Covid-19 outbreak, many people are working from home (WfH) and shop using online platforms. Geographic Information Science and System (GIS—Geographic Information Systems) technologies and IT services are used to

analyse spatial and temporal data collected from various organisations to ge<sup>t</sup> better insights regarding current trends and model future trends and their impacts on communities and well-being. This big shift from traditional workplaces and shopping towards WfH and online shopping underscores the importance of further developing smart city infrastructures and deploying geospatial technologies to address future needs. This paper identifies selected trends in geospatial science, particularly the applications of GIS. In addition, this paper observes newly developed online apps such as ArcGIS Urban, used for predicting future impacts of developing urban areas in three dimensions. These technologies provide useful tools for smart city stakeholders and users to predict future implications of the proposed plans and collaborate with the organisations to achieve more appropriate outcomes, considering various criteria including sustainable development goals (SDGs) at various scales.

Digital twins of cities have recently attracted much attention as a useful virtual platform that captures changes to the physical environment in the city and all associated activities and movements [3]. Figure 1a–d schematically illustrate a digital–physical twin of a smart city, including the data managemen<sup>t</sup> process and dashboard development. Figure 1e,f shows some examples developed by the first author. Using sensors, Unmanned Aerial Vehicles (UAVs), satellites, and different technologies, the physical entities, activities, behaviours, and interactions are required to be connected to a digital model [3] for a more realistic data platform. Integration of the digital twin as a 3D representation of the city and associated information can be used for the assessment of the performance of the city and selected construction projects using a data managemen<sup>t</sup> system. Apps such as ArcGIS urban can also help us to evaluate the impact of a new project before it is implemented. Such digital twins, in conjunction with sensors and other advanced data collection technologies, can help in better modelling the strategic behaviours of agents [4].

This editorial is divided into two sections: (i) the development of advanced tools such as miniaturization of sensors and mobile scanners, geospatial AI, Unmanned Aerial Vehicles (UAVs), geospatial AR apps, and Light Detection and Ranging (Lidar); as well as (ii) applications of the tools in cities and products such as Self-Driving Vehicles and Smart Cities. Finally, the papers included in this Special Issue are reviewed.

**Figure 1.** Demonstrating digital–physical twin at the city scale, (**a**) city physical twin; (**b**) city digital twin; (**c**) data managemen<sup>t</sup> and interactions; (**d**) dashboard development based on computation; (**e**) integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) for a digital model of a proposed building; (**f**) dashboard developed by the first author used for making better insights from data.

## **2. Advanced Technologies**

This section introduces the different tools and technologies that are critical to create a digital twin for a smart city. These critical technologies include network technology, sensors, artificial intelligence, big data, and Lidar technologies [5]. The integration of sensors with GIS in city analytics is a new geospatial trend that can significantly improve smart city technology. While the cost of utilizing such sensors is high, another emerging trend is to miniaturize sensors and data acquisition tools such as innovative bee-sized drones and mini satellites to generate useful data in an efficient and cost-effective manner. Selected technologies useful to improve the connectivity in smart cities which can be used for digital-physical twin are discussed as follows but can be further investigated in the future (See Figure 1).
