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The Greenland Ice Sheet (GrIS) rapid mass loss is primarily driven by an increase in meltwater runoff, which highlights the importance of understanding the formation, evolution and impact of meltwater features on the ice sheet. Buried lakes are meltwater features that contain liquid water and exist under layers of snow, firn, and/or ice. These lakes are invisible in optical imagery, challenging the analysis of their evolution and implication for larger GrIS dynamics and mass change. Here, we present a method that uses a convolutional neural network, a deep learning method, to automatically detect buried lakes across the GrIS. For the years 2018 and 2019 (which represent low and high melt years, respectively), we compare total areal extent of both buried and surface lakes across six regions, and use a regional climate model to explain the spatial and temporal differences. We find that the total buried lake extent after the 2019 melt season is 56% larger than after the 2018 melt season across the entire ice sheet. Northern Greenland observes the largest increase in buried lake extent after the 2019 melt season, which we attribute to late-summer surface melt and high autumn temperatures. This study helps to provide additional perspective on the potential role of meltwater on GrIS dynamics and mass loss.