  The widespread availability of satellite images has allowed researchers to
model complex systems such as disease dynamics. However, many satellite images
have missing values due to measurement defects, which render them unusable
without data imputation. For example, the scanline corrector for the LANDSAT 7
satellite broke down in 2003, resulting in a loss of around 20\% of its data.
Inpainting involves predicting what is missing based on the known pixels and is
an old problem in image processing, classically based on PDEs or interpolation
methods, but recent deep learning approaches have shown promise. However, many
of these methods do not explicitly take into account the inherent
spatiotemporal structure of satellite images. In this work, we cast satellite
image inpainting as a natural meta-learning problem, and propose using
convolutional neural processes (ConvNPs) where we frame each satellite image as
its own task or 2D regression problem. We show ConvNPs can outperform classical
methods and state-of-the-art deep learning inpainting models on a scanline
inpainting problem for LANDSAT 7 satellite images, assessed on a variety of in
and out-of-distribution images.
