Reconfigurable intelligent surface (RIS) technology and its integration into existing wireless networks have recently attracted much interest. While an important use case of said technology consists in mounting RISs onto unmanned aerial vehicles (UAVs) to support the terrestrial infrastructure in post-disaster scenarios, the current literature lacks an analytical framework that captures the networks' topological aspects. Therefore, our study borrows stochastic geometry tools to estimate both the average and local coverage probability of a wireless network aided by an aerial RIS (ARIS); in particular, the surviving terrestrial base stations (TBSs) are modeled by means of an inhomogeneous Poisson point process, while the UAV is assumed to hover above the disaster epicenter. Our framework captures important aspects such as the TBSs' altitude, the fact that they may be in either line-of-sight or non-line-of-sight condition with a given node, and the Nakagami- m fading conditions of wireless links. By leveraging said aspects we accurately evaluate three possible scenarios, where TBSs are either: (i) not aided, (ii) aided by an ARIS, or (iii) aided by an aerial base station (ABS). Our selected numerical results reflect various situations, depending on parameters such as the environment's urbanization level, disaster radius, and the UAV's altitude.