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Availability and quality of administrative data on irrigation technology varies greatly across jurisdictions. Technology choice, however, will influence the parameters of coupled human-hydrological systems. Equally, changing parameters in the coupled system may drive technology adoption. Here we develop and demonstrate a deep learning approach to locate a particularly important irrigation technology—center pivot irrigation systems—throughout the Ogallala Aquifer. The model does not rely on super computers and thus provides a model for an accessible baseline to train and deploy on other geographies. We further demonstrate that accounting for the technology can improve the insights in both economic and hydrological models.