  The focus of this paper is using a convolutional machine learning model with
a modified U-Net structure for creating land cover classification mapping based
on satellite imagery. The aim of the research is to train and test
convolutional models for automatic land cover mapping and to assess their
usability in increasing land cover mapping accuracy and change detection. To
solve these tasks, authors prepared a dataset and trained machine learning
models for land cover classification and semantic segmentation from satellite
images. The results were analysed on three different land classification
levels. BigEarthNet satellite image archive was selected for the research as
one of two main datasets. This novel and recent dataset was published in 2019
and includes Sentinel-2 satellite photos from 10 European countries made in
2017 and 2018. As a second dataset the authors composed an original set
containing a Sentinel-2 image and a CORINE land cover map of Estonia. The
developed classification model shows a high overall F\textsubscript{1} score of
0.749 on multiclass land cover classification with 43 possible image labels.
The model also highlights noisy data in the BigEarthNet dataset, where images
seem to have incorrect labels. The segmentation models offer a solution for
generating automatic land cover mappings based on Sentinel-2 satellite images
and show a high IoU score for land cover classes such as forests, inland waters
and arable land. The models show a capability of increasing the accuracy of
existing land classification maps and in land cover change detection.
