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Rising sea levels and shifting weather patterns linked to climate change have increased the frequency and severity of exposures to extreme events, particularly flooding, across the globe. A paucity of high-quality longitudinal data has limited scientific understanding of the implications of these exposures and their aftermath for population health and well-being over the long term. Focusing on the 2004 Indian Ocean Tsunami, we measure small-scale geographic area exposures to initial destruction and gradual reconstruction of built and natural environments using convolutional neural network methods applied to high-resolution satellite imagery. We illustrate the value of combining these measures with individual-level data on health and well-being that we collected over two decades starting before the tsunami as part of a population-representative longitudinal household survey, the Study of the Tsunami Aftermath and Recovery.