  In this paper we present our work on developing an automated system for land
cover classification. This system takes a multiband satellite image of an area
as input and outputs the land cover map of the area at the same resolution as
the input. For this purpose convolutional machine learning models were trained
in the task of predicting the land cover semantic segmentation of satellite
images. This is a case of supervised learning. The land cover label data were
taken from the CORINE Land Cover inventory and the satellite images were taken
from the Copernicus hub. As for the model, U-Net architecture variations were
applied. Our area of interest are the Ionian islands (Greece). We created a
dataset from scratch covering this particular area. In addition, transfer
learning from the BigEarthNet dataset [1] was performed. In [1] simple
classification of satellite images into the classes of CLC is performed but not
segmentation as we do. However, their models have been trained into a dataset
much bigger than ours, so we applied transfer learning using their pretrained
models as the first part of out network, utilizing the ability these networks
have developed to extract useful features from the satellite images (we
transferred a pretrained ResNet50 into a U-Res-Net). Apart from transfer
learning other techniques were applied in order to overcome the limitations set
by the small size of our area of interest. We used data augmentation (cutting
images into overlapping patches, applying random transformations such as
rotations and flips) and cross validation. The results are tested on the 3 CLC
class hierarchy levels and a comparative study is made on the results of
different approaches.
