Historical maps are the primary source for spatial information. These documents play an important role in the conservation and understanding of history. However, a broad access to these documents is limited, firstly because only a few copies were digitized, and secondly because these documents often have a dated language and visuals. In a world used to satellite images and GPS navigation in mobile phones, we hypothesize if an architecture using neural network can reinterpret a historical map, presenting its information as a satellite image from the past. Although previous works have been handling information from historical maps, as well as creating reinterpretations of such documents, this work presents an architecture of generative adversarial networks for transferring style between satellite images and historical maps. The literature demonstrates the use of these networks in the creation of convincing artificial media for a common observer, popularized by the DeepFake trend in the last years. This work also proposes a creation of a satellite imagery collection - for training the networks - whose aspects would be transferred to the final synthesized output. We present an architecture to synthesize satellite images from the past, and discuss its steps. Finally, we present and discuss synthesized satellite images of Recife, Brazil, from the past and how this technique can be a tool for other areas of study and the public debate.