Recent evidence has indicated that spatial representations, such as large-scale geographical maps, can be retrieved from natural language alone through cognitively plausible distributional-semantic models based on non-spatial associative-learning mechanisms. Here, we demonstrate that analogous spatial maps can be extracted from purely linguistic data even at the medium-scale level. Our results indeed show that it is possible to retrieve the underground maps of five European cities from linguistic data, suggesting in turn that the ability to reconstruct spatial maps from language does not strictly depend on the scale being mapped. Furthermore, we show that different spatial representations (i.e., with information encoded either in terms of relative spatial distances or absolute locations defined by coordinate axes) can be retrieved from natural language. These findings contribute to a growing body of research that challenges the traditional view of cognitive maps as exclusively relying on specialized spatial computations and highlight the importance of non-spatial associative-learning mechanisms within the linguistic environment in the setting of spatial representations.