  The acquisition of accurate rainfall distribution in space is an important
task in hydrological analysis and natural disaster pre-warning. However, it is
impossible to install rain gauges on every corner. Spatial interpolation is a
common way to infer rainfall distribution based on available raingauge data.
However, the existing works rely on some unrealistic pre-settings to capture
spatial correlations, which limits their performance in real scenarios. To
tackle this issue, we propose the SSIN, which is a novel data-driven
self-supervised learning framework for rainfall spatial interpolation by mining
latent spatial patterns from historical observation data. Inspired by the Cloze
task and BERT, we fully consider the characteristics of spatial interpolation
and design the SpaFormer model based on the Transformer architecture as the
core of SSIN. Our main idea is: by constructing rich self-supervision signals
via random masking, SpaFormer can learn informative embeddings for raw data and
then adaptively model spatial correlations based on rainfall spatial context.
Extensive experiments on two real-world raingauge datasets show that our method
outperforms the state-of-the-art solutions. In addition, we take traffic
spatial interpolation as another use case to further explore the performance of
our method, and SpaFormer achieves the best performance on one large real-world
traffic dataset, which further confirms the effectiveness and generality of our
method.
