  In recent years, the geospatial industry has been developing at a steady
pace. This growth implies the addition of satellite constellations that produce
a copious supply of satellite imagery and other Remote Sensing data on a daily
basis. Sometimes, this information, even if in some cases we are referring to
publicly available data, it sits unaccounted for due to the sheer size of it.
Processing such large amounts of data with the help of human labour or by using
traditional automation methods is not always a viable solution from the
standpoint of both time and other resources.
  Within the present work, we propose an approach for creating a multi-modal
and spatio-temporal dataset comprised of publicly available Remote Sensing data
and testing for feasibility using state of the art Machine Learning (ML)
techniques. Precisely, the usage of Convolutional Neural Networks (CNN) models
that are capable of separating different classes of vegetation that are present
in the proposed dataset. Popularity and success of similar methods in the
context of Geographical Information Systems (GIS) and Computer Vision (CV) more
generally indicate that methods alike should be taken in consideration and
further analysed and developed.

Computer Vision and Pattern Recognition;Artificial Intelligence