  Fine-grained population distribution data is of great importance for many
applications, e.g., urban planning, traffic scheduling, epidemic modeling, and
risk control. However, due to the limitations of data collection, including
infrastructure density, user privacy, and business security, such fine-grained
data is hard to collect and usually, only coarse-grained data is available.
Thus, obtaining fine-grained population distribution from coarse-grained
distribution becomes an important problem. To tackle this problem, existing
methods mainly rely on sufficient fine-grained ground truth for training, which
is not often available for the majority of cities. That limits the applications
of these methods and brings the necessity to transfer knowledge between
data-sufficient source cities to data-scarce target cities.
  In knowledge transfer scenario, we employ single reference fine-grained
ground truth in target city, which is easy to obtain via remote sensing or
questionnaire, as the ground truth to inform the large-scale urban structure
and support the knowledge transfer in target city. By this approach, we
transform the fine-grained population mapping problem into a one-shot transfer
learning problem. In this paper, we propose a novel one-shot transfer learning
framework PSRNet to transfer spatial-temporal knowledge across cities from the
view of network structure, the view of data, and the view of optimization.
  Experiments on real-life datasets of 4 cities demonstrate that PSRNet has
significant advantages over 8 state-of-the-art baselines by reducing RMSE and
MAE by more than 25%. Our code and datasets are released in Github
(https://github.com/erzhuoshao/PSRNet-CIKM).
