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The availability of frequent and cost-free satellite images is in growingdemand in the research world. Such satellite constellations as Landsat 8 andSentinel-2 provide a massive amount of valuable data daily. However, thediscrepancy in the sensors' characteristics of these satellites makes itsenseless to use a segmentation model trained on either dataset and applied toanother, which is why domain adaptation techniques have recently become anactive research area in remote sensing. In this paper, an experiment of domainadaptation through style-transferring is conducted using the HRSemI2I model tonarrow the sensor discrepancy between Landsat 8 and Sentinel-2. This paper'smain contribution is analyzing the expediency of that approach by comparing theresults of segmentation using domain-adapted images with those withoutadaptation. The HRSemI2I model, adjusted to work with 6-band imagery, showssignificant intersection-over-union performance improvement for both mean andper class metrics. A second contribution is providing different schemes ofgeneralization between two label schemes - NALCMS 2015 and CORINE. The firstscheme is standardization through higher-level land cover classes, and thesecond is through harmonization validation in the field.

Semantically-consistent Landsat 8 image to Sentinel-2 image translation for alpine areas