  Due to the rapid growth of urban areas in the past decades, it has become
increasingly important to model and monitor urban growth in mega cities.
Although several researchers have proposed models for simulating urban growth,
they have been primarily dependent on various manually selected spatial and
nonspatial explanatory features for building models. A practical difficulty
with this approach is manual selection procedure, which tends to make model
design process laborious and non-generic. Despite the fact that explanatory
features provide us with the understanding of a complex process, there has been
no standard set of features in urban growth prediction over which scholars have
consensus. Hence, design and deploying of systems for urban growth prediction
have remained challenging tasks. In order to reduce the dependency on human
devised features, we have proposed a novel End-to-End prediction framework to
represent remotely sensed satellite data in terms of rules of a cellular
automata model in order to improve the performance of urban growth prediction.
Using our End-to-End framework, we have achieved superior performance in Figure
of Merit, Producer's accuracy, User's accuracy, and Overall accuracy metrics
respectively over existing learning based methods.
