SEMANTIC SEGMENTATION USING A UNET ARCHITECTURE ON SENTINEL-2 DATA

This paper presents the development of a methodological framework, based on deep learning, for the efficient mapping of main land cover classes (built-up, vegetation, barren land, water body) on different urban and suburban landscapes. In particular, the proposed framework integrates the superpixel segmentation (an essential procedure) with deep learning. A combination of spectral bands and indices is introduced to produce optimal results, ensuring adequate discrimination between built-up and barren land classes. A UNET architecture is implemented, which can learn the characteristics of main land cover classes from the input data that can be deployed from a Colab notebook without excessive computational needs. The resulted classifications depict promising accuracy values (above 90%).