In sensing-enabled mobile infrastructure, the network itself acts as a whole sensor by leveraging radio data or signals collected within Base Stations (BSs). This data is exploited for the development of data-driven machine learning solutions to augment network's capabilities. Nevertheless, largescale qualitative data is required for achieving high accuracy learning. However, their training phase leads to prohibitive cost and heavy constraints on data collection and storage that are not desirable for network. To overcome this problem, we propose to use synthetic data instead of real data for training machine learning models to avoid high cost data sharing/storage. In this paper, we are interested in real-life Environment Sensing Network in a context of limited data amount sharing. We focus on Indoor-Outdoor Detection (IOD) using unsupervised machine learning classification models. For this purpose, experiments are conducted following the paradigm of Training on Synthetic data and Testing on Real Data (TSTR). We conduct a comparative study of four well-known generative models, that are able to generate synthetic 3GPP radio data with similar distribution than the source data. We investigate the quality of these synthetic generated radio data according to three dimensions: distribution similarity, data variability and detection capability. The classification models trained with synthetic generated data are tested in real-life context to infer whether a user connected to the network is inside or outside a building. The study shows convincing results with an Indoor/Outdoor unsupervised classification performance up to 80% of F1−score like in real-life data training scenarios.