ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 9–14, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-9-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 9–14, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-9-2021

  17 Jun 2021

17 Jun 2021

TOWARDS FINE-GRAINED ROAD MAPS EXTRACTION USING SENTINEL-2 IMAGERY

C. Ayala1, C. Aranda1, and M. Galar2 C. Ayala et al.
  • 1Tracasa Instrumental, Calle Cabárceno, 6, 31621 Sarriguren, Navarra, Spain
  • 2Institute of Smart Cities (ISC), Public University of Navarre, Campus de Arrosadía s/n, 31006, Pamplona, Spain

Keywords: Sentinel-2, Remote Sensing, Road Network Extraction, Deep Learning, Convolutional Neural Networks

Abstract. Nowadays, it is highly important to keep road maps up-to-date since a great deal of services rely on them. However, to date, these labours have demanded a great deal of human attention due to their complexity. In the last decade, promising attempts have been carried out to fully-automatize the extraction of road networks from remote sensing imagery. Nevertheless, the vast majority of methods rely on aerial imagery (< 1 m), whose costs are not yet affordable for maintaining up-to-date maps. This work proves that it is also possible to accurately detect roads using high resolution satellite imagery (10 m). Accordingly, we have relied on Sentinel-2 imagery considering its freely availability and the higher revisit times compared to aerial imagery. It must be taken into account that the lack of spatial resolution of this sensor drastically increases the difficulty of the road detection task, since the feasibility to detect a road depends on its width, which can reach sub-pixel size in Sentinel-2 imagery. For that purpose, a new deep learning architecture which combines semantic segmentation and super-resolution techniques is proposed. As a result, fine-grained road maps at 2.5 m are generated from Sentinel-2 imagery.