  Satellite Image Time Series (SITS) of the Earth's surface provide detailed
land cover maps, with their quality in the spatial and temporal dimensions
consistently improving. These image time series are integral for developing
systems that aim to produce accurate, up-to-date land cover maps of the Earth's
surface. Applications are wide-ranging, with notable examples including
ecosystem mapping, vegetation process monitoring and anthropogenic land-use
change tracking. Recently proposed methods for SITS classification have
demonstrated respectable merit, but these methods tend to lack native
mechanisms that exploit the temporal dimension of the data; commonly resulting
in extensive data pre-processing contributing to prohibitively long training
times. To overcome these shortcomings, Temporal CNNs have recently been
employed for SITS classification tasks with encouraging results. This paper
seeks to survey this method against a plethora of other contemporary methods
for SITS classification to validate the existing findings in recent literature.
Comprehensive experiments are carried out on two benchmark SITS datasets with
the results demonstrating that Temporal CNNs display a superior performance to
the comparative benchmark algorithms across both studied datasets, achieving
accuracies of 95.0\% and 87.3\% respectively. Investigations into the Temporal
CNN architecture also highlighted the non-trivial task of optimising the model
for a new dataset.
