A physics-constrained cGAN is trained as an image-to-image translator on remote-sensing layers to recover spatial sensitivities of urban land-use change to macroeconomic indicators via backpropagation gradients.
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Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks
A physics-constrained cGAN is trained as an image-to-image translator on remote-sensing layers to recover spatial sensitivities of urban land-use change to macroeconomic indicators via backpropagation gradients.