The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 195–200, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-195-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 195–200, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-195-2020

  21 Aug 2020

21 Aug 2020

OPERATIONAL PIPELINE FOR A GLOBAL CLOUD-FREE MOSAIC AND CLASSIFICATION OF SENTINEL-2 IMAGES

M. Swaine1, C. Smit1, S. Tripodi2, G. Fonteix2, Y. Tarabalka2, L. Laurore2, and J. Hyland1 M. Swaine et al.
  • 1LuxCarta South Africa, Cape Town, South Africa
  • 2LuxCarta Technology, Mouans Sartoux, France

Keywords: deep learning, optical satellite images, cloud detection, U-net, classification

Abstract. Global Earth observation from satellite images is an active research topic, driven by numerous applications, such as telecommunications, defence, natural hazard monitoring and urban management. The recently launched twin Sentinel-2 satellites acquire 13-band optical data with a 2–5 day revisit time, freely available for any use, and thus very valuable for global Earth observation. In this paper, we present a completely automatic operational chain for a global cloud-free mosaic and classification of Sentinel-2 images. The proposed pipeline enables producing the world 10-m cloud-free mosaic, and a 6-class landcover classification map.