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Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules / Yongjiu Feng in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)
[article]
Titre : Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules Type de document : Article/Communication Auteurs : Yongjiu Feng, Auteur Année de publication : 2017 Article en page(s) : pp 1198 - 1219 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] algorithme génétique
[Termes IGN] analyse diachronique
[Termes IGN] automate cellulaire
[Termes IGN] base de règles
[Termes IGN] changement d'utilisation du sol
[Termes IGN] données GPS
[Termes IGN] jointure spatiale
[Termes IGN] Kiangsou (Chine)
[Termes IGN] modèle de simulation
[Termes IGN] prédiction
[Termes IGN] régression logistique
[Termes IGN] simulation
[Termes IGN] zone urbaineRésumé : (auteur) A novel generalized pattern search (GPS)-based cellular automata (GPS-CA) model was developed to simulate urban land-use change in a GIS environment. The model is built on a fitness function that computes the difference between the observed results produced from remote-sensing images and the simulated results produced by a general CA model. GPS optimization incorporating genetic algorithms (GAs) searches for the minimum difference, i.e. the smallest accumulated residuals, in fitting the CA transition rules. The CA coefficients captured by the GPS method have clear physical meanings that are closely associated with the dynamic mechanisms of land-use change. The GPS-CA model was applied to simulate urban land-use change in Kunshan City in the Yangtze River Delta from 2000 to 2015. The results show that the GPS method had a smaller root mean squared error (0.2821) than a logistic regression (LR) method (0.5256) in fitting the CA transition rules. The GPS-CA model thus outperformed the LR-CA model, with an overall accuracy improvement of 4.7%. As a result, the GPS-CA model should be a superior tool for modeling land-use change as well as predicting future scenarios in response to different conditions to support the sustainable urban development. Numéro de notice : A2017-244 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1287368 En ligne : http://dx.doi.org/10.1080/13658816.2017.1287368 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85180
in International journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 1198 - 1219[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2017031 RAB Revue Centre de documentation En réserve L003 Disponible A hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model / Rachel Whitsed in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)
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Titre : A hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model Type de document : Article/Communication Auteurs : Rachel Whitsed, Auteur ; Lisa T. Smallbone, Auteur Année de publication : 2017 Article en page(s) : pp 717 - 737 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme génétique
[Termes IGN] arbre (flore)
[Termes IGN] automate cellulaire
[Termes IGN] croissance des arbres
[Termes IGN] dynamique de la végétation
[Termes IGN] étalonnage des données
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modèle de croissance végétale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] sous-bois
[Termes IGN] Victoria (Australie)
[Vedettes matières IGN] ForesterieRésumé : (Auteur) Cellular automata (CA) models are commonly used to model vegetation dynamics, with the genetic algorithm (GA) being one method of calibration. This article investigates different GA settings, as well as the combination of a GA with a local optimiser to improve the calibration effort. The case study is a pattern-calibrated CA to model vegetation regrowth in central Victoria, Australia. We tested 16 GA models, varying population size, mutation rate, and level of allowable mutation. We also investigated the effect of applying a local optimiser, the Nelder‒Mead Downhill Simplex (NMDS) at GA convergence. We found that using a decreasing mutation rate can reduce computational cost while avoiding premature GA convergence, while increasing population size does not make the GA more efficient. The hybrid GA-NMDS can also reduce computational cost compared to a GA alone, while also improving the calibration metric. We conclude that careful consideration of GA settings, including population size and mutation rate, and in particular the addition of a local optimiser, can positively impact the efficiency and success of the GA algorithm, which can in turn lead to improved simulations using a well-calibrated CA model. Numéro de notice : A2017-081 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1231315 En ligne : http://dx.doi.org/10.1080/13658816.2016.1231315 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84344
in International journal of geographical information science IJGIS > vol 31 n° 3-4 (March-April 2017) . - pp 717 - 737[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2017021 RAB Revue Centre de documentation En réserve L003 Disponible 079-2017022 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : An Introduction to Machine Learning Type de document : Guide/Manuel Auteurs : Miroslav Kubat, Auteur Mention d'édition : 2ème édition Editeur : Springer International Publishing Année de publication : 2017 ISBN/ISSN/EAN : 978-3-319-63913-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme génétique
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage par renforcement
[Termes IGN] apprentissage profond
[Termes IGN] arbre de décision
[Termes IGN] classificateur
[Termes IGN] classification barycentrique
[Termes IGN] classification bayesienne
[Termes IGN] exploration de données
[Termes IGN] raisonnement inductif
[Termes IGN] réseau neuronal artificiel
[Termes IGN] test de performanceMots-clés libres : Bayesian classifiersboostingcomputational learning theorydecision treesgenetic algorithmslinear and polynomial classifiersnearest neighbor classifierneural networksperformance evaluationreinforcement learningstatistical learningtime-varying classes, imbalanced representationartificial intelligencemachine learningdata miningdeep learningunsupervised learning Résumé : (Auteur) [Introduction] This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work. Numéro de notice : 26276 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel DOI : 10.1007/978-3-319-63913-0 En ligne : https://doi.org/10.1007/978-3-319-63913-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94915 An immune genetic algorithm to buildings displacement in cartographic generalization / Yageng Sun in Transactions in GIS, vol 20 n° 4 (August 2016)
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Titre : An immune genetic algorithm to buildings displacement in cartographic generalization Type de document : Article/Communication Auteurs : Yageng Sun, Auteur ; Qingsheng Guo, Auteur ; Yuangang Liu, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 585 - 612 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme génétique
[Termes IGN] bâtiment
[Termes IGN] contrainte géométrique
[Termes IGN] contrainte relationnelle
[Termes IGN] déplacement d'objet géographique
[Termes IGN] généralisation automatique de données
[Termes IGN] généralisation cartographique
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Spatial conflicts may occur when map data are displayed at a scale smaller than that of the source map. This study applies the displacement operator in cartographic generalization to resolve such spatial conflicts and to improve the clarity and legibility of map. The immune genetic algorithm (IGA) is used in this study for buildings displacement to solve conflicts. IGA is based on the genetic algorithm (GA) and employs the self-adjusting mechanism of antibody concentration to enhance population diversity. Meanwhile, the elitism retention strategy is adopted in IGA to guarantee that the best individual (antibody) is not lost and destroyed in the next generation to strengthen convergence efficiency. The compared experiment between IGA and GA shows that the displacement result produced by IGA performs better than GA. Finally, in order to make the displaced map more attractive to cartographers, two constraints – the building alignment constraint and building tangent relation constraint – are applied in IGA to restrict the buildings’ displacement. The same experimental data are adopted to prove that the improved IGA is useful for maintaining the two constraints. Numéro de notice : A2016--053 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12165 En ligne : http://dx.doi.org/10.1111/tgis.12165 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83771
in Transactions in GIS > vol 20 n° 4 (August 2016) . - pp 585 - 612[article]Autonomous ortho-rectification of very high resolution imagery using SIFT and genetic algorithm / Pramod Kumar Konugurthi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 5 (May 2016)
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Titre : Autonomous ortho-rectification of very high resolution imagery using SIFT and genetic algorithm Type de document : Article/Communication Auteurs : Pramod Kumar Konugurthi, Auteur ; Raghavendra Kune, Auteur ; Ravi Nooka, Auteur ; Venkatraman Sarma, Auteur Année de publication : 2016 Article en page(s) : pp 377 - 388 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] algorithme génétique
[Termes IGN] appariement d'images
[Termes IGN] chaîne de traitement
[Termes IGN] image à très haute résolution
[Termes IGN] orthorectification
[Termes IGN] point d'appui
[Termes IGN] SIFT (algorithme)Résumé : (Auteur) Ortho-rectification of very high resolution imagery from agile platforms using Rigorous Sensor Model / Rational Functional Model is quite challenging and demands a fair amount of interactivity in Ground Control Point (GCP) identification/selection for refining the model and for final product evaluation. The paper proposes achieving complete automation in the ortho-rectification process by eliminating all the interactive components, and incorporating fault tolerance mechanisms within the model to make the process robust and reliable. The key aspects proposed in this paper are: two stage Scale Invariant Feature Transform (SIFT) based matching to obtain a large numbers of checkpoints using much coarser resolution images such as Landsat/ETM+, followed by a GA to select the right combination of minimal GCPS based on minimizing Root Mean Square Error (RMSE) and maximizing the area covered under GCPS, and finally, a decision rule based product evaluation to make the process operate in an "autonomous closed loop mode". The method is generic and has been tested on hundreds of Cartosat-1/2 images, and has achieved above 90% reliability with sub-pixel relative error of reference data. Numéro de notice : A2016-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.5.377 En ligne : http://dx.doi.org/10.14358/PERS.82.5.377 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81279
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 5 (May 2016) . - pp 377 - 388[article]Optimisation d'un service d'autopartage de véhicules électriques / Amine Ait-Ouahmed in Revue internationale de géomatique, vol 26 n° 2 (avril - juin 2016)PermalinkPermalinkOptimal spatial land-use allocation for limited development ecological zones based on the geographic information system and a genetic ant colony algorithm / Nan Mi in International journal of geographical information science IJGIS, vol 29 n° 12 (December 2015)PermalinkClassification of remotely sensed images using the geneSIS fuzzy segmentation algorithm / Stelios Mylonas in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)PermalinkGlobal optimization of GNSS station reference networks / David Coulot in GPS solutions, vol 19 n° 4 (october 2015)PermalinkApplication d'algorithmes génétiques à la détermination d'orbites optimales pour GRASP / Arnaud Pollet in XYZ, n° 144 (septembre - novembre 2015)PermalinkRegional land-use allocation using a coupled MAS and GA model: from local simulation to global optimization, a case study in Caidian District, Wuhan, China / Man Yuan in Cartography and Geographic Information Science, vol 41 n° 4 (September 2014)PermalinkOrbit computation of the TELECOM-2D satellite with a genetic algorithm / Florent Deleflie in Proceedings of the International astronomical union, vol 9 S310 (Juillet 2014)PermalinkActive learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes / Christos Yiakoumettis in Geoinformatica, vol 18 n° 1 (January 2014)PermalinkPermalinkRecherche des sous-réseaux d’antennes VLBI et de radio‐sources extra‐galactiques par algorithmes génétiques / Serge Nyoka (2014)PermalinkPermalinkClassification automatique des images satellitaires optimisée par l'algorithme des chauves-souris / Soumia Benmostefa in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)PermalinkUtility of the wavelet transform for LAI estimation using hyperspectral data / Asim Banskota in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 7 (July 2013)PermalinkOptimisation de transport à la demande dans des territoires polarisés / Rémy Chevrier in Cartes & Géomatique, n° 215 (mars 2013)Permalink2D arrangement-based hierarchical spatial partitioning: an application to pedestrian network generation / Murat Yirci (2013)PermalinkFirst attempt of orbit determination of SLR satellites and space debris using genetic algorithms / Florent Deleflie (2013)PermalinkPermalinkFusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes / J. Im in Geocarto international, vol 27 n° 5 (August 2012)PermalinkApplication des algorithmes génétiques à la recherche de sous-réseaux de stations de télémétrie laser / David Coulot in Bulletin d'information scientifique et technique de l'IGN, n° 77 (avril 2011)Permalink