###### Abstract  
Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such as their coverage. While several projects aim at collecting cellular network data via crowdsourcing observations, none focuses on information about the structure of the networks, which is paramount to correctly model their topology. The difficulty of such a modeling is exacerbated by the inherent differences among cellular technologies, the strong spatio-temporal nature of positioning, and the continuously evolving configuration of the networks. In this paper, we first show how to synthesize a detailed conceptual schema of cellular networks on the basis of the signal fingerprints collected by devices. We turned it into a logical one, and we exploited that to build a relational spatio-temporal database capable of supporting a crowdsourced collection of data. Next, we populated the database with heterogeneous cellular observations originating from multiple sources. In addition, we illustrate how the developed system allows us to properly deal with the evolution of the network configuration, e.g., by detecting cell renaming phenomena and by making it possible to correct inconsistent measurements coming from mobile devices, fostering positioning tasks. Finally, we provide a wide range of basic, spatial, and temporal analyses about the arrangement of the cellular network and its evolution over time, demonstrating how the developed system can be used to reconstruct and maintain a deep knowledge of the cellular network, possibly starting from crowdsourced information only. spatio-temporal database; outdoor positioning; fingerprinting; crowdsourcing 202323352022335202335202352  
satellites. Unfortunately, GNSS have some drawbacks. On the one hand, their signals are not always available. This is the case with environments such as indoor areas and urban canyons, where the performance of GNSS is significantly reduced, considering both the time required to obtain a position fix and the overall localization accuracy [3; 4; 5]. On the other hand, a GNSS may only be used sparely on battery-powered devices, due to its high energy consumption [6; 7]. To overcome these limitations, hybrid positioning systems have been proposed, which pair GNSS with other localization technologies that can be used to replace or estimate the satellite-based position whenever necessary [8]. One of the most significant complementary/alternative solutions is offered by signal fingerprinting [9; 10; 11], where different signals received by a device are compared with those recorded in a radio map containing measurements taken at known locations to estimate the current position. The cellular radio communication network, which appeared for the first time in the early 90s and then went through successive technological generations (GSM, UMTS, LTE, etc. ), is nowadays the most widespread and used communication network, spanning the entire globe and allowing the transmission of both voice and data signals. The term cellular radio denotes the deployment of a large number of low powered cell towers for signal transmission; each one has a limited transmission area called a cell and is associated with a specific radio frequency. Given the global coverage of the cellular network and the common usage of its receivers, cellular signals are suitable for fingerprinting [12]. In such cases, a fingerprint consists of the collection of the signal strengths of the observed cell towers. It follows that they have a strong spatio-temporal characterization: the collected fingerprint is associated with a position (ground truth or estimated), and two fingerprints sensed in the same place may differ over time due to user equipment, environment, or cellular network changes. Fingerprinting heavily relies on comprehensive and accurate knowledge of the cellular network configuration.