The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-119-2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-119-2022
30 May 2022
 | 30 May 2022

SEMANTIC SEGMENTATION USING A UNET ARCHITECTURE ON SENTINEL-2 DATA

I. Kotaridis and M. Lazaridou

Keywords: CNNs, UNET, superpixel segmentation, Python, Sentinel-2

Abstract. 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%).