During the last decade, the average mobile wireless data usage per person has tremendously increased. An even faster growth of the traffic demand is expected for the incoming years, due to several factors such as the increasing global population, the spread of the Internet of things (IoT), and the development of advanced technologies that require a higher amount of data. While mobile communication technologies have rapidly evolved to meet this need in the most usual situations, it is expected that the sixth generation (6G) of mobile connectivity will be the first one paying considerable attention to under-connected environments such as low-income, remote, or disaster-struck regions. Many specialized researchers and entrepreneurs are trying to design and implement alternative network architectures specifically meant for enhancing the performances of the current telecommunication (telecom) infrastructure. In particular, the use of aerial base stations (ABSs) has received considerable attention due to the main advantages of easy deployability and low-cost that are typical of unmanned aerial vehicles (UAVs), which are available in several fashions depending on the application; moreover, UAVs are also eligible to carry reflective intelligent surfaces (RISs), which represent a promising technology that allows to reflect signals towards specific directions. Another possibility that we have investigated consists in integrating the transceivers inside or atop existing rural wind turbine (WT) towers, in order to increase the coverage radius while avoiding the cost of building a separate telecom infrastructure. A powerful mathematical tool for evaluating the performance metrics of either terrestrial, aerial, or vertical heterogeneous wireless networks is stochastic geometry (SG), since it can be used to model the locations of the base stations (BSs) according to tractable spatial distributions (with either a fixed or a random cardinality) in order to imitate the typical deployments of the nodes made in realistic scenarios; in particular, in this work we focus on rural and post-disaster situations. SG makes use of point processes to model networks' topologies. The developed spatial models, in turn, allow us to analyze the quality of service (QoS) experienced by the typical user served by the proposed networks. To this extent, we creatively and efficiently studied our inhomogeneous systems by making use of what we call the indicator method, meaning that we do not subdivide the ground plane in multiple homogeneous sub-regions, but we use indicator functions to provide general expressions that are valid over the entire ground plane. To prove the effectiveness of the novel architectures, insightful comparisons with the conventional ones are presented.