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Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change / Hao Wu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 5-6 (May - June 2019) . - pp 1040 - 1061 Titre : Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Zhen Li, Auteur ; Keith C. Clarke, Auteur ; Wenzhong Shi, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1040 - 1061 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] modèle de Markov [Termes descripteurs IGN] simulation Résumé : (Auteur) Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size. Numéro de notice : A2019-443 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1568441 date de publication en ligne : 18/02/2019 En ligne : https://doi.org/10.1080/13658816.2019.1568441 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92777 [article]

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Designing an integrated urban growth prediction model: a scenario-based approach for preserving scenic landscapes / Sepideh Saeidi in Geocarto international, vol 33 n° 12 (December 2018)  

Public [article]  inGeocarto international > vol 33 n° 12 (December 2018) . - pp 1381 - 1397 Titre : Designing an integrated urban growth prediction model: a scenario-based approach for preserving scenic landscapes Type de document : Article/Communication Auteurs : Sepideh Saeidi, Auteur ; Seyed Hamed Mirkarimi, Auteur ; Marjan Mohammadzadeh, Auteur ; Abdolrassoul Salmanmahiny, Auteur ; Colin Arrowsmith, Auteur Année de publication : 2018 Article en page(s) : pp 1381 - 1397 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] Iran [Termes descripteurs IGN] méthode d'étalonnage basée sur le modèle [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] paysage urbain [Termes descripteurs IGN] Perceptron multicouche [Termes descripteurs IGN] réseau neuronal artificiel Résumé : (auteur) This study demonstrates the integration of landscape aesthetic quality and probable urban growth patterns in urban landscape modelling. This was performed using SLEUTH as a scenario-based urban growth model in Gorgan City of Iran. Future urbanization was predicted under developing three different scenarios including historical, managed and aesthetically sound urban growth up to the year 2030. Multi-Layer Perceptron neural network model was conducted for mapping the aesthetic suitability of the study area. The aesthetic suitability layer was used in the third scenario of SLEUTH model as the excluded layer to protect the scenic patches in future. The results showed that by correct implementation of urban growth policies, 323 ha in the second scenario and 650 ha in the third scenario would be saved. This integrated model would help the planners for a better management of urban landscapes as a Spatial Decision Support System. Numéro de notice : A2019-050 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1353647 date de publication en ligne : 24/07/2017 En ligne : https://doi.org/10.1080/10106049.2017.1353647 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92071 [article]

Hierarchical cellular automata for visual saliency / Yao Qin in International journal of computer vision, vol 126 n° 7 (July 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 7 (July 2018) . - pp 751 - 770 Titre : Hierarchical cellular automata for visual saliency Type de document : Article/Communication Auteurs : Yao Qin, Auteur ; Mengyang Feng, Auteur ; Huchuan Lu, Auteur ; Garrison W. Cottrell, Auteur Année de publication : 2018 Article en page(s) : pp 751 - 770 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] zone saillante 3D Résumé : (Auteur) Saliency detection, finding the most important parts of an image, has become increasingly popular in computer vision. In this paper, we introduce Hierarchical Cellular Automata (HCA)—a temporally evolving model to intelligently detect salient objects. HCA consists of two main components: Single-layer Cellular Automata (SCA) and Cuboid Cellular Automata (CCA). As an unsupervised propagation mechanism, Single-layer Cellular Automata can exploit the intrinsic relevance of similar regions through interactions with neighbors. Low-level image features as well as high-level semantic information extracted from deep neural networks are incorporated into the SCA to measure the correlation between different image patches. With these hierarchical deep features, an impact factor matrix and a coherence matrix are constructed to balance the influences on each cell’s next state. The saliency values of all cells are iteratively updated according to a well-defined update rule. Furthermore, we propose CCA to integrate multiple saliency maps generated by SCA at different scales in a Bayesian framework. Therefore, single-layer propagation and multi-scale integration are jointly modeled in our unified HCA. Surprisingly, we find that the SCA can improve all existing methods that we applied it to, resulting in a similar precision level regardless of the original results. The CCA can act as an efficient pixel-wise aggregation algorithm that can integrate state-of-the-art methods, resulting in even better results. Extensive experiments on four challenging datasets demonstrate that the proposed algorithm outperforms state-of-the-art conventional methods and is competitive with deep learning based approaches. Numéro de notice : A2018-413 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-017-1062-2 date de publication en ligne : 23/02/2018 En ligne : https://doi.org/10.1007/s11263-017-1062-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90896 [article]

A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area / Guodong Du in International journal of geographical information science IJGIS, vol 32 n° 3-4 (March - April 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 3-4 (March - April 2018) . - pp 757 - 782 Titre : A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area Type de document : Article/Communication Auteurs : Guodong Du, Auteur ; Kong Joo Shin, Auteur ; Liang Yuan, Auteur ; Shunsuke Managi, Auteur Année de publication : 2018 Article en page(s) : pp 757 - 782 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] Tokyo (Japon) [Termes descripteurs IGN] utilisation du sol [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) Urban multiple land use change (LUC) modelling enables the realistic simulation of LUC processes in complex urban systems; however, such modelling suffers from technical challenges posed by complicated transition rules and high spatial heterogeneity when predicting the LUC of a highly developed area. Tree-based methods are powerful tools for addressing this task, but their predictive capabilities need further examination. This study integrates tree-based methods and cellular automata to simulate multiple LUC processes in the Greater Tokyo Area. We examine the predictive capability of 4 tree-based models – bagged trees, random forests, extremely randomised trees (ERT) and bagged gradient boosting decision trees (bagged GBDT) – on transition probability prediction for 18 land use transitions derived from 8 land use types. We compare the predictive power of a tree-based model with multi-layer perceptron (MLP) and among themselves. The results show that tree-based models generally perform better than MLP, and ERT significantly outperforms the three other tree-based models. The outstanding predictive performance of ERT demonstrates the advantages of introducing bagging ensemble and a high degree of randomisation into transition probability modelling. In addition, through variable importance evaluation, we found the strongest explanatory powers of neighbourhood characteristics for all land use transitions; however, the size of the impacts depends on the neighbourhood land use type and the neighbourhood size. Furthermore, socio-economic and policy factors play important roles in transitions ending with high-rise buildings and transitions related to industrial areas. Numéro de notice : A2018-044 Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1410550 En ligne : https://doi.org/10.1080/13658816.2017.1410550 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89266 [article]

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Calibrating a Land Parcel Cellular Automaton (LP-CA) for urban growth simulation based on ensemble learning / Yimin Chen in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 11-12 (November - December 2017) . - pp 2480 - 2504 Titre : Calibrating a Land Parcel Cellular Automaton (LP-CA) for urban growth simulation based on ensemble learning Type de document : Article/Communication Auteurs : Yimin Chen, Auteur ; Xiaoping Liu, Auteur ; Xia Li, Auteur Année de publication : 2017 Article en page(s) : pp 2480 - 2504 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] étalonnage de modèle [Termes descripteurs IGN] Kouangtoung (Chine) [Termes descripteurs IGN] maillage [Termes descripteurs IGN] parcelle cadastrale [Termes descripteurs IGN] simulation Résumé : (Auteur) The reliability of raster cellular automaton (CA) models for fine-scale land change simulations has been increasingly questioned, because regular pixels/grids cannot precisely represent irregular geographical entities and their interactions. Vector CA models can address these deficiencies due to the ability of the vector data structure to represent realistic urban entities. This study presents a new land parcel cellular automaton (LP-CA) model for simulating urban land changes. The innovation of this model is the use of ensemble learning method for automatic calibration. The proposed model is applied in Shenzhen, China. The experimental results indicate that bagging-Naïve Bayes yields the highest calibration accuracy among a set of selected classifiers. The assessment of neighborhood sensitivity suggests that the LP-CA model achieves the highest simulation accuracy with neighbor radius r = 2. The calibrated LP-CA is used to project future urban land use changes in Shenzhen, and the results are found to be consistent with those specified in the official city plan. Numéro de notice : A2017-702 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1367004 En ligne : https://doi.org/10.1080/13658816.2017.1367004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88083 [article]

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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)  

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Integrating cellular automata and Markov techniques to generate urban development potential surface : a study on Kolkata agglomeration / Biswajit Mondal in Geocarto international, vol 32 n° 4 (April 2017)  

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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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Knowledge transfer for large-scale urban growth modeling based on formal concept analysis / Jinyao Lin in Transactions in GIS, vol 20 n° 5 (October 2016)  

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Simulating urban growth processes by integrating cellular automata model and artificial optimization in Binhai New Area of Tianjin, China / Fengmei Yao in Geocarto international, vol 31 n° 5 - 6 (May - June 2016)  

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SLEUTH* : un modèle d’expansion urbaine scénario-dépendant / Omar Doukari in Revue internationale de géomatique, vol 26 n° 1 (janvier - mars 2016)

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An exploration of future patterns of the contributions to OpenStreetMap and development of a contribution index / Jamal Jokar Arsanjani in Transactions in GIS, vol 19 n° 6 (December 2015)  

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A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS / Abubrakr A. A. Al Sharif in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)  

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Characterization of neighborhood sensitivity of an irregular cellular automata model of urban growth / Khila R. Dahal in International journal of geographical information science IJGIS, vol 29 n° 3 (March 2015)  

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How much past to see the future: a computational study in calibrating urban cellular automata / Ivan Blecic in International journal of geographical information science IJGIS, vol 29 n° 3 (March 2015)  

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