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Changes in shrub and tree cover concurrent with rising air temperatures are a widespread phenomenon in Arctic–Boreal ecosystems. The expansion of tall shrubs and trees can alter ground thermal regimes and soil moisture impacting permafrost and biogeochemical cycling. Changes in shrub and tree cover can be difficult to characterize with limited in-situ observations and moderate/coarse resolution satellite imagery, thereby posing challenges in disentangling changes in vegetation growth from shifts in vegetation composition. We pair high resolution historical (KeyHole9 1971) and current satellite imagery (WorldView-3 2020) with a convolutional neural network approach to predict forest, shrubland, and surface water cover within a region of the Kolyma lowland (171 km ^2 ) in eastern Siberia. The overall accuracy of the predictions was 0.90 for 1971 and 0.92 for 2020. We found an overall net increase in shrubland cover of 14 km ^2 (8% of study extent) and little net change in forest cover, but changes in both land cover classes were highly heterogenous across the landscape. Increases in shrubland cover were highest in proximity to surface water (