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Multi-label class assignment in land-use modelling / Hichem Omrani in International journal of geographical information science IJGIS, vol 29 n° 6 (June 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 6 (June 2015) . - pp 1023 - 1041 Titre : Multi-label class assignment in land-use modelling Type de document : Article/Communication Auteurs : Hichem Omrani, Auteur ; Fahed Abdallah, Auteur ; Omar Charif, Auteur Année de publication : 2015 Article en page(s) : pp 1023 - 1041 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] alignement semi-dirigé [Termes IGN] analyse multivariée [Termes IGN] apprentissage automatique [Termes IGN] classification barycentrique [Termes IGN] image aérienne [Termes IGN] Luxembourg [Termes IGN] modélisation [Termes IGN] plus proche voisin, algorithme du [Termes IGN] utilisation du sol Résumé : (Auteur) During the last two decades, a variety of models have been applied to understand and predict changes in land use. These models assign a single-attribute label to each spatial unit at any particular time of the simulation. This is not realistic because mixed use of land is quite common. A more detailed classification allowing the modelling of mixed land use would be desirable for better understanding and interpreting the evolution of the use of land. A possible solution is the multi-label (ML) concept where each spatial unit can belong to multiple classes simultaneously. For example, a cluster of summer houses at a lake in a forested area should be classified as water, forest and residential (built-up). The ML concept was introduced recently, and it belongs to the machine learning field. In this article, the ML concept is introduced and applied in land-use modelling. As a novelty, we present a land-use change model that allows ML class assignment using the k nearest neighbour (kNN) method that derives a functional relationship between land use and a set of explanatory variables. A case study with a rich data-set from Luxembourg using biophysical data from aerial photography is described. The model achieves promising results based on the well-known ML evaluation criteria. The application described in this article highlights the value of the multi-label k nearest neighbour method (MLkNN) for land-use modelling. Numéro de notice : A2015-599 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1008004 En ligne : https://doi.org/10.1080/13658816.2015.1008004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78013 [article]