  Training robust supervised deep learning models for many geospatial
applications of computer vision is difficult due to dearth of class-balanced
and diverse training data. Conversely, obtaining enough training data for many
applications is financially prohibitive or may be infeasible, especially when
the application involves modeling rare or extreme events. Synthetically
generating data (and labels) using a generative model that can sample from a
target distribution and exploit the multi-scale nature of images can be an
inexpensive solution to address scarcity of labeled data. Towards this goal, we
present a deep conditional generative model, called VAE-Info-cGAN, that
combines a Variational Autoencoder (VAE) with a conditional Information
Maximizing Generative Adversarial Network (InfoGAN), for synthesizing
semantically rich images simultaneously conditioned on a pixel-level condition
(PLC) and a macroscopic feature-level condition (FLC). Dimensionally, the PLC
can only vary in the channel dimension from the synthesized image and is meant
to be a task-specific input. The FLC is modeled as an attribute vector in the
latent space of the generated image which controls the contributions of various
characteristic attributes germane to the target distribution. An interpretation
of the attribute vector to systematically generate synthetic images by varying
a chosen binary macroscopic feature is explored. Experiments on a GPS
trajectories dataset show that the proposed model can accurately generate
various forms of spatio-temporal aggregates across different geographic
locations while conditioned only on a raster representation of the road
network. The primary intended application of the VAE-Info-cGAN is synthetic
data (and label) generation for targeted data augmentation for computer
vision-based modeling of problems relevant to geospatial analysis and remote
sensing.
