  The continuous representation of spatiotemporal data commonly relies on using
abstract data types, such as \textit{moving regions}, to represent entities
whose shape and position continuously change over time. Creating this
representation from discrete snapshots of real-world entities requires using
interpolation methods to compute in-between data representations and estimate
the position and shape of the object of interest at arbitrary temporal points.
Existing region interpolation methods often fail to generate smooth and
realistic representations of a region's evolution. However, recent advancements
in deep learning techniques have revealed the potential of deep models trained
on discrete observations to capture spatiotemporal dependencies through
implicit feature learning.
  In this work, we explore the capabilities of Conditional Variational
Autoencoder (C-VAE) models to generate smooth and realistic representations of
the spatiotemporal evolution of moving regions. We evaluate our proposed
approach on a sparsely annotated dataset on the burnt area of a forest fire. We
apply compression operations to sample from the dataset and use the C-VAE model
and other commonly used interpolation algorithms to generate in-between region
representations. To evaluate the performance of the methods, we compare their
interpolation results with manually annotated data and regions generated by a
U-Net model. We also assess the quality of generated data considering temporal
consistency metrics.
  The proposed C-VAE-based approach demonstrates competitive results in
geometric similarity metrics. It also exhibits superior temporal consistency,
suggesting that C-VAE models may be a viable alternative to modelling the
spatiotemporal evolution of 2D moving regions.
