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Similarity weighted instance-based learning for the generation of transition potentials in land use change modeling / F. Sangermano in Transactions in GIS, vol 14 n° 5 (October 2010)  

Public [article]  inTransactions in GIS > vol 14 n° 5 (October 2010) . - pp 569 - 580 Titre : Similarity weighted instance-based learning for the generation of transition potentials in land use change modeling Type de document : Article/Communication Auteurs : F. Sangermano, Auteur ; J. Ronald Eastman, Auteur ; H. Zhu, Auteur Année de publication : 2010 Article en page(s) : pp 569 - 580 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] algorithme d'apprentissage [Termes IGN] analyse comparative [Termes IGN] apprentissage automatique [Termes IGN] classification barycentrique [Termes IGN] déboisement [Termes IGN] modélisation spatio-temporelle [Termes IGN] Perceptron multicouche [Termes IGN] similitude [Termes IGN] traitement de données localisées [Termes IGN] utilisation du sol Résumé : (Auteur) Land use change models are increasingly being used to evaluate the effect of land change on climate and biodiversity and to generate scenarios of deforestation. Although many methods are available to model land transition potentials, they are usually not user-friendly and require the specification of many parameters, making the task difficult for decision makers not familiar with the tools, as well as making the process difficult to interpret. In this article we propose a simple method for modeling transition potentials. SimWeight is an instance-based learning algorithm based on the logic of the K-Nearest Neighbor algorithm. The method identifies the relevance of each driver variable and predicts the transition potential of locations given known instances of change. A case study was used to demonstrate and validate the method. Comparison of results with the Multi-Layer Perceptron neural network (MLP) suggests that SimWeight performs similarly in its capacity to predict transition potentials, without the need for complex parameters. Another advantage of SimWeight is that it is amenable to parallelization for deployment on a cloud computing platform. Numéro de notice : A2010-496 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2010.01226.x Date de publication en ligne : 23/11/2010 En ligne : https://doi.org/10.1111/j.1467-9671.2010.01226.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30689 [article]

FloodwayGIS : An ArcGIS Visualization Environment to Remodel a Floodway / S. Selvanathan in Transactions in GIS, vol 14 n° 5 (October 2010)  

Public [article]  inTransactions in GIS > vol 14 n° 5 (October 2010) . - pp 671 - 688 Titre : FloodwayGIS : An ArcGIS Visualization Environment to Remodel a Floodway Type de document : Article/Communication Auteurs : S. Selvanathan, Auteur ; R. Dymond, Auteur Année de publication : 2010 Article en page(s) : pp 671 - 688 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] ArcGIS [Termes IGN] canal [Termes IGN] crue [Termes IGN] inondation [Termes IGN] logiciel de visualisation [Termes IGN] risque naturel [Termes IGN] Triangulated Irregular Network Résumé : (Auteur) Floodway modeling has been performed extensively using HECRAS in floodplain studies. The model output is typically exported in GIS format and the floodway boundaries are overlaid on other spatial data to further edit or remodel the floodway to meet FEMA and local development requirements. In this article, a tightly coupled system comprised of a commercial GIS (ArcGIS) and HECRAS is presented. FloodwayGIS provides a comprehensive visual environment to edit, remodel, spatially analyze, and map floodway boundaries. The environment uses the HECRAS executable engine for every remodeling iteration. Four different encroachment editing options are provided within FloodwayGIS, which eliminates the need for a modeler to switch between HECRAS and GIS in the floodway modeling process, and results in savings of modeling time. FloodwayGIS also provides a mapping algorithm based on TIN intersection to produce smooth floodway boundaries that can be mapped in Digital Flood Insurance Rate Maps (DFIRMs) with minor editing. Numéro de notice : A2010-498 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2010.01225.x Date de publication en ligne : 23/11/2010 En ligne : https://doi.org/10.1111/j.1467-9671.2010.01225.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30690 [article]

A road network selection process based on data enrichment and structure detection / Guillaume Touya in Transactions in GIS, vol 14 n° 5 (October 2010)    

Public [article]  inTransactions in GIS > vol 14 n° 5 (October 2010) . - pp 595 - 614 Titre : A road network selection process based on data enrichment and structure detection Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur Année de publication : 2010 Article en page(s) : pp 595 - 614 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carrefour [Termes IGN] Clarity (plateforme de généralisation) [Termes IGN] échangeur routier [Termes IGN] généralisation de base de données [Termes IGN] prise en compte du contexte [Termes IGN] réseau routier [Termes IGN] théorie des graphes [Vedettes matières IGN] Généralisation Résumé : (Auteur) In the context of geographical database generalization, this article deals with a generic process for road network selection. It is based on the geographical context, which is made explicit, and on the preservation of characteristic structure. It relies on literature that is adapted and collected. The first step is to detect significant structures and patterns of the road network such as roundabouts or highway interchanges. It allows the initial dataset to be enriched with explicit geographic structures that were implicit in the initial data. It helps both to make the geographical context explicit and to preserve characteristic structures. Then this enrichment is used as knowledge input for the following step: that is, the selection of roads in rural areas using graph theory techniques. After that, urban roads are selected by means of a block aggregation complex algorithm. Continuity between urban and rural areas is guaranteed by modelling continuity using strokes. Finally, the previously detected characteristic structures are typified to maintain their properties in the selected network. This automated process has been fully implemented on Clarity™ and tested on large datasets. Numéro de notice : A2010-639 Affiliation des auteurs : COGIT (1988-2011) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2010.01215.x Date de publication en ligne : 23/11/2010 En ligne : https://doi.org/10.1111/j.1467-9671.2010.01215 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90688 [article]

Documents numériques

peut être téléchargé A Road Network Selection Process - preprint Adobe Acrobat PDF