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The focus of this paper is using a convolutional machine learning model witha modified U-Net structure for creating land cover classification mapping basedon satellite imagery. The aim of the research is to train and testconvolutional models for automatic land cover mapping and to assess theirusability in increasing land cover mapping accuracy and change detection. Tosolve these tasks, authors prepared a dataset and trained machine learningmodels for land cover classification and semantic segmentation from satelliteimages. The results were analysed on three different land classificationlevels. BigEarthNet satellite image archive was selected for the research asone of two main datasets. This novel and recent dataset was published in 2019and includes Sentinel-2 satellite photos from 10 European countries made in2017 and 2018. As a second dataset the authors composed an original setcontaining a Sentinel-2 image and a CORINE land cover map of Estonia. Thedeveloped classification model shows a high overall F\textsubscript{1} score of0.749 on multiclass land cover classification with 43 possible image labels.The model also highlights noisy data in the BigEarthNet dataset, where imagesseem to have incorrect labels. The segmentation models offer a solution forgenerating automatic land cover mappings based on Sentinel-2 satellite imagesand show a high IoU score for land cover classes such as forests, inland watersand arable land. The models show a capability of increasing the accuracy ofexisting land classification maps and in land cover change detection.

Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification