  Transfer Learning methods are widely used in satellite image segmentation
problems and improve performance upon classical supervised learning methods. In
this study, we present a semantic segmentation method that allows us to make
land cover maps by using transfer learning methods. We compare models trained
in low-resolution images with insufficient data for the targeted region or zoom
level. In order to boost performance on target data we experiment with models
trained with unsupervised, semi-supervised and supervised transfer learning
approaches, including satellite images from public datasets and other unlabeled
sources. According to experimental results, transfer learning improves
segmentation performance 3.4% MIoU (Mean Intersection over Union) in rural
regions and 12.9% MIoU in urban regions. We observed that transfer learning is
more effective when two datasets share a comparable zoom level and are labeled
with identical rules; otherwise, semi-supervised learning is more effective by
using the data as unlabeled. In addition, experiments showed that HRNet
outperformed building segmentation approaches in multi-class segmentation.
