Multi-scale methodology to map grey and green structures in urban areas using Pléiades images and existing geographic data

  • Jean Nabucet LETG-Rennes COSTEL UMR CNRS 6554, Université Rennes
  • Simon Rougier LIVE UMR CNRS 7362, Université de Strasbourg
  • Julien Deniau LIVE UMR CNRS 7362, Université de Strasbourg
  • Léo Vétillard LETG-Rennes COSTEL UMR CNRS 6554, Université Rennes 2,
  • Emilie Hanson IGEAT, Université Libre de Bruxelles
  • Omar Benarchid IGEAT, Université Libre de Bruxelles
  • Eléonore Wolff IGEAT, Université Libre de Bruxelles
  • Laurence Hubert-Moy LETG-Rennes COSTEL UMR CNRS 6554, Université Rennes
  • Anne Puissant Université de Strasbourg - Laboratoire LIVE


Identification and monitoring of urban fabric and preservation of existing ecosystems have become major issues to maintain or increase biodiversity in areas under urban influence in most of European cities. While many studies have shown the interest of using optical remotely sensed data for that purpose, a consolidated and reproducible methodological framework was still missing. In this context, a multi-scale methodology has been proposed in the framework of the project VALI-URB to map built-up and vegetated land features in urban and suburban areas based on Pleiades images and existing ancillary data (vector databases or the Open Street Map database). The objective of this paper is to highlight the interest of using land cover/use maps derived from Pleiades images and vector databases to semi-automatically characterize grey and green infrastructures at a scale of 1:10,000. First results are presented on two medium-sized cities with different urban forms: Strasbourg and Rennes (France).


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Comment citer
NABUCET, Jean et al. Multi-scale methodology to map grey and green structures in urban areas using Pléiades images and existing geographic data. Revue Française de Photogrammétrie et de Télédétection, [S.l.], n. 209, p. 95-101, jan. 2015. ISSN 1768-9791. Disponible à l'adresse : >>. Date de consultation : 30 mai 2020


morphologie urbaine, imagerie Pléiades, Données urban structure, Pleiades imagery, ancillary data, object-based image classification