## 4 Critical Literature Review
(2020) analysed temperature variation on six urban surfaces: water, tree canopy, grass, brick pavement, asphalt and marble in the city of Nanjing. Their results showed that surfaces of artificial origin released more sensible heat and that air humidity was negatively correlated with temperature variations [116]. [PERSON] et al. (2017) generated an orthogonal image of land surface temperature using thermal imaging and computer vision in the city of Tokyo. The experiment was repeated in two summers in 2007 and 2013 and the results showed an increase in land areas in 2007 that had been urbanised in 2013 [117]. The same conclusion was provided by [PERSON] et al. (2017) in Taipei city where the diurnal variation of city temperature was proven to increase with the rate of urbanization [118]. Urban-scale thermography has also been widely used for the detection of residential roof heat losses and the identification of homes where energy savings can be made [119]. [PERSON] et al. (2013) presented a novel automated method to calculate the cost of energy loss for building diagnostics by combining aerial thermography, manual thermography and an energy performance augmented reality (EPAR) model based on computational fluid dynamics (CFD). Based on the detected air leakage, heat losses and heat gains caused by poor insulation or air infiltration/extraction and the associated energy costs were automatically calculated [120]. In 2016, the ChoT project aimed to analyse the potential of aerial thermal imagery to produce large-scale datasets for energy efficiency and policy analysis in urban environments in the city of Bologna. The project was entitled \"the Challenge of remote sensing Thermography as indicator of energy efficiency of buildings\" (ChoT). It was funded by the Italian Ministry of Education, University and Research (MIUR) in the framework of the SIR 2014 (Scientific Independence of young Researchers) programme [121]. In addition, UAVs have demonstrated their feasibility in analysing and reducing various threats to human and environmental health such as air pollution in urban areas [112]. Emissions of volatile organic compounds from the petrochemical industry and vehicle exhaust gases contribute to the formation of ozone and organic aerosols. Some authors analysed air quality using an energy-efficient, real-time air quality monitoring system using aerial and ground-based detection [122; 123; 124]. The maximum exponent of urban energy efficiency is the smart city, a type of urban development based on sustainability. These are models of interconnected networks in the city that are fed back with large amounts of real time data [125]. Smart cities contain intelligent elements that allow measuring aspects related to the environment, quality of life, public safety and disaster management. To achieve these tasks, advanced collaborative technologies such as UAVs, machine learning and the Internet of Things (IoT) are required to lead to prudent management of natural resources [126]. An integrated technology of connected and autonomous vehicles was proposed for various uses such as the improvement of wireless capacity and coverage between cellular networks, urban video-surveillance of road and pedestrian traffic and transport of people and goods, among others [50; 127]. In the next few years, it is expected that urban legislation will be relaxed to allow for large-scale delivery of products by air. Currently, delivery companies are already registering patents for storage and loading systems for UAVs in the built environment [6; 128]. For further reading comprehension, Table 6 summarises the main applications and payloads identified by cluster.