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Throughout evolution, the human visual system has adapted to efficiently encode several environmental constants to deal with the huge complexity involved in representing large-scale spatial environments. Being terrestrial animals, these constants reflect a specific ground-based viewpoint. For example, people show a strong affinity for detecting a perceptual upright layout relative to the horizon. But, what happens when humans leave this terrestrial perspective, for example when taking a flight, or looking down from the Empire State building? Are the mechanisms originally evolved for terrestrial scene recognition also recruited for the recognition of novel large-scale environments people rarely encounter on a daily basis? We propose that studying how people learn to recognize aerial scene images can reveal how the scene recognition system develops through experience and shed light on the putative mechanisms underlying its malleability. We conducted an intensive six-session behavioral training study in which naive participants learned to categorize manmade and natural scenes at a specific-subordinate level (‘suspension bridge’). Scene images depicted real-world places from a terrestrial and an aerial viewpoint, allowing us to establish how people learn to categorize two visually-different images as the same environment. We found that performance constantly improved over the first five training sessions, with greater learning for the aerial compared to the terrestrial scenes. This viewpoint performance gap manifested more for manmade than natural scenes. In addition to memory improvements, we also found learning transfer, the hallmark of perceptual learning. Performance in the sixth session (in which participants categorized scenes they had not been trained with) was significantly better compared to the first session, and equivalent to average performance across training-sessions. Our findings provide novel evidence for the potential mechanisms underlying plasticity in the scene recognition system, showing both memory and perception contribute to experience-based enhancements in scene recognition.