  In the remote sensing community, Land Use Land Cover (LULC) classification
with satellite imagery is a main focus of current research activities. Accurate
and appropriate LULC classification, however, continues to be a challenging
task. In this paper, we evaluate the performance of multi-temporal (monthly
time series) compared to mono-temporal (single time step) satellite images for
multi-label classification using supervised learning on the RapidAI4EO dataset.
As a first step, we trained our CNN model on images at a single time step for
multi-label classification, i.e. mono-temporal. We incorporated time-series
images using a LSTM model to assess whether or not multi-temporal signals from
satellites improves CLC classification. The results demonstrate an improvement
of approximately 0.89% in classifying satellite imagery on 15 classes using a
multi-temporal approach on monthly time series images compared to the
mono-temporal approach. Using features from multi-temporal or mono-temporal
images, this work is a step towards an efficient change detection and land
monitoring approach.
