This is not a computer vision problem, because spatial correlation between geographic cells is destroyed. Figure modified from Mignan & Broccardo (2019c), representing the deep learning workflow of DeVries et al. – Instead of having nx times ny features for n mainshock samples, the authors used roughly nx times ny times n geographic cell samples (and 12 stress-based features).

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This is not a computer vision problem, because spatial correlation between geographic cells is destroyed. Figure modified from Mignan & Broccardo (2019c), representing the deep learning workflow of DeVries et al. - Instead of having nx times ny features for n mainshock samples, the authors used roughly nx times ny times n geographic cell samples (and 12 stress-based features).

This is not a computer vision problem, because spatial correlation between geographic cells is destroyed. Figure modified from Mignan & Broccardo (2019c), representing the deep learning workflow of DeVries et al. – Instead of having nx times ny features for n mainshock samples, the authors used roughly nx times ny times n geographic cell samples (and 12 stress-based features).

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