  Time-series remote sensing data offer a rich source of information that can
be used in a wide range of applications, from monitoring changes in land cover
to surveilling crops, coastal changes, flood risk assessment, and urban sprawl.
This paper addresses the challenge of using time-series satellite images to
predict urban expansion. Building upon previous work, we propose a novel
two-step approach based on semantic image segmentation in order to predict
urban expansion. The first step aims to extract information about urban regions
at different time scales and prepare them for use in the training step. The
second step combines Convolutional Neural Networks (CNN) with Long Short Term
Memory (LSTM) methods in order to learn temporal features and thus predict
urban expansion. In this paper, experimental results are conducted using
several multi-date satellite images representing the three largest cities in
Saudi Arabia, namely: Riyadh, Jeddah, and Dammam. We empirically evaluated our
proposed technique, and examined its results by comparing them with
state-of-the-art approaches. Following this evaluation, we determined that our
results reveal improved performance for the new-coupled CNN-LSTM approach,
particularly in terms of assessments based on Mean Square Error, Root Mean
Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and
overall classification accuracy.
