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<publisher>HAL CCSD</publisher>
<title lang=en>Structural knowledge learning from maps for supervised land cover/use classification: Application to the monitoring of land cover/use maps in French Guiana</title>
<creator>Bayoudh, Meriam</creator>
<creator>Roux, Emmanuel</creator>
<creator>Richard, Gilles</creator>
<creator>Nock, Richard</creator>
<contributor>Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>UMR 228 Espace-Dev, Espace pour le développement ; Institut de Recherche pour le Développement (IRD) - Université de Perpignan Via Domitia (UPVD) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Université d'Avignon et des Pays de Vaucluse (UAPV) - Université de la Réunion (UR) - Université de Montpellier (UM) - Université de Guyane (UG) - Université des Antilles (Pôle Martinique) ; Université des Antilles (UA) - Université des Antilles (UA) - Université des Antilles (Pôle Guadeloupe) ; Université des Antilles (UA)</contributor>
<contributor>Institut de recherche en informatique de Toulouse (IRIT) ; Institut National Polytechnique [Toulouse] (INP) - Université Toulouse 1 Capitole (UT1) - Université Toulouse 2 (UT2) - Université Paul Sabatier - Toulouse 3 (UPS) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>Centre de Recherche en Economie, Gestion, Modélisation et Informatique Appliquée (CEREGMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<description>International audience</description>
<source>ISSN: 0098-3004</source>
<source>Computers and Geosciences</source>
<publisher>Elsevier</publisher>
<identifier>hal-01369822</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01369822</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01369822/document</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01369822/file/CAGEO-D-13-00538R1_article.pdf</identifier>
<source>https://hal.archives-ouvertes.fr/hal-01369822</source>
<source>Computers and Geosciences, Elsevier, 2015, 76, pp.31-40. 〈10.1016/j.cageo.2014.08.013〉</source>
<identifier>DOI : 10.1016/j.cageo.2014.08.013</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cageo.2014.08.013</relation>
<language>en</language>
<subject lang=en> Geographic Information System</subject>
<subject lang=en>Supervised classification</subject>
<subject lang=en> Machine learning</subject>
<subject lang=en> Inductive Logic Programming (ILP)</subject>
<subject lang=en> Land cover map</subject>
<subject>[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]</subject>
<subject>[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]</subject>
<subject>[SDE.MCG] Environmental Sciences/Global Changes</subject>
<subject>[SDU.STU.GP] Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph]</subject>
<subject>[SHS.GEO] Humanities and Social Sciences/Geography</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>The number of satellites and sensors devoted to Earth observation has become increasingly elevated, delivering extensive data, especially images. At the same time, the access to such data and the tools needed to process them has considerably improved. In the presence of such data flow, we need automatic image interpretation methods, especially when it comes to the monitoring and prediction of environmental and societal changes in highly dynamic socio-environmental contexts. This could be accomplished via artificial intelligence. The concept described here relies on the induction of classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system, combined with a multi-class classification procedure. This methodology was used to monitor changes in land cover/use of the French Guiana coastline. One hundred and fifty-eight classification rules were induced from 3 diachronic land cover/use maps including 38 classes. These rules were expressed in first order logic language, which makes them easily understandable by non-experts. A 10-fold cross-validation gave significant average values of 84.62%, 99.57% and 77.22% for classification accuracy, specificity and sensitivity, respectively. Our methodology could be beneficial to automatically classify new objects and to facilitate object-based classification procedures.</description>
<date>2015</date>
<contributor>ANR-10-EQPX-0020/10-EQPX-0020, GEOSUD, GEOSUD : Infrastructure nationale d’imagerie satellitaire pour la recherche sur l’environnement et les territoires et ses applications à la gestion et aux politiques publiques(2010)</contributor>
<contributor>European Project : 30492, CARTAM-SAT</contributor>
<relation>info:eu-repo/grantAgreement//30492/EU/CARtographie du Territoire AMazonien: des Satellites aux AcTeurs - Dynamic mapping of Amazonian Territories: from Satellites to Actors/CARTAM-SAT</relation>
</dc>
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