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A hidden Markov model for matching spatial networks / Benoit Costes in Journal of Spatial Information Science (JoSIS), n° 18 (2019)  

Public [article]  inJournal of Spatial Information Science (JoSIS) > n° 18 (2019) . - pp 57 - 89 Titre : A hidden Markov model for matching spatial networks Type de document : Article/Communication Auteurs : Benoit Costes , Auteur ; Julien Perret , Auteur Année de publication : 2019 Projets : SODUCO / Perret, Julien Article en page(s) : pp 57 - 89 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de sensibilité [Termes IGN] appariement de réseaux [Termes IGN] modèle de Markov caché [Termes IGN] modèle topologique réseau [Termes IGN] réseau ferroviaire [Termes IGN] réseau fluvial [Termes IGN] réseau routier Résumé : (auteur) Datasets of the same geographic space at different scales and temporalities are increasingly abundant, paving the way for new scientific research. These datasets require data integration, which implies linking homologous entities in a process called data matching that remains a challenging task, despite a quite substantial literature, because of data imperfections and heterogeneities. In this paper, we present an approach for matching spatial networks based on a hidden Markov model (HMM) that takes full benefit of the underlying topology of networks. The approach is assessed using four heterogeneous datasets (streets, roads, railway, and hydrographic networks), showing that the HMM algorithm is robust in regards to data heterogeneities and imperfections (geometric discrepancies and differences in level of details) and adaptable to match any type of spatial networks. It also has the advantage of requiring no mandatory parameters, as proven by a sensitivity exploration, except a distance threshold that filters potential matching candidates in order to speed-up the process. Finally, a comparison with a commonly cited approach highlights good matching accuracy and completeness. Numéro de notice : A2019-274 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5311/JOSIS.2019.18.489 Date de publication en ligne : 01/07/2019 En ligne : https://doi.org/10.5311/JOSIS.2019.18.489 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95337 [article]

OpenStreetMap in GIScience / Jamal Jokar Arsanjani (2015)

Public Titre : OpenStreetMap in GIScience : experiences, research, and applications Type de document : Monographie Auteurs : Jamal Jokar Arsanjani, Éditeur scientifique ; Alexander Zipf, Éditeur scientifique ; Peter Mooney, Éditeur scientifique ; Marco Helbich, Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2015 Collection : Lecture notes in Geoinformation and Cartography, ISSN 1863-2246 Importance : 324 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-319-14279-1 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] analyse de données [Termes IGN] base de données foncières [Termes IGN] cartographie collaborative [Termes IGN] données localisées des bénévoles [Termes IGN] données statistiques [Termes IGN] graphe [Termes IGN] impact social [Termes IGN] itinéraire [Termes IGN] modèle topologique réseau [Termes IGN] occupation du sol [Termes IGN] OpenStreetMap [Termes IGN] qualité des données Index. décimale : 37.10 Bases de données géographiques Résumé : (Editeur) This edited volume presents a collection of lessons learned with, and research conducted on, OpenStreetMap, the goal being to promote the project’s integration. The respective chapters address a) state-of-the-art and cutting-edge approaches to data quality analysis in OpenStreetMap, b) investigations on understanding OpenStreetMap contributors and the nature of their contributions, c) identifying patterns of contributions and contributors, d) applications of OpenStreetMap in different domains, e) mining value-added knowledge and information from OpenStreetMap, f) limitations in the analysis OpenStreetMap data, and g) integrating OpenStreetMap with commercial and non-commercial datasets. The book offers an ideal opportunity to present and disseminate a number of cutting-edge developments and applications in the field of geography, spatial statistics, GIS, social science, and cartography. Note de contenu : Part 1 - Data management and quality Part 2 - Social context Part 3 - Network modeling and routing Part 4 - Land management and urban form Part 5 - Outlook Numéro de notice : 22415 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Recueil / ouvrage collectif Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79437

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• OpenStreetMap in GIScience : experiences, research, and applications. Inferring the Scale of OpenStreetMap Features / Guillaume Touya (2015)  

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Spatially modelling pathways of migratory birds for nature reserve site selection / J.A. Downs in International journal of geographical information science IJGIS, vol 22 n° 6-7 (june 2008)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 22 n° 6-7 (june 2008) . - pp 687 - 702 Titre : Spatially modelling pathways of migratory birds for nature reserve site selection Type de document : Article/Communication Auteurs : J.A. Downs, Auteur ; M.W. Horner, Auteur Année de publication : 2008 Article en page(s) : pp 687 - 702 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] Aves [Termes IGN] habitat animal [Termes IGN] migration animale [Termes IGN] modèle topologique réseau [Termes IGN] outil d'aide à la décision [Termes IGN] réserve naturelle [Termes IGN] système d'information géographique [Termes IGN] zone humide Résumé : (Auteur) Conserving habitat for migratory birds presents a unique set of challenges, because multiple reserves distributed across space are required for complete protection of single species. We developed the Flight Leg Allocation Problem (FLAP), a distance-constrained shortest path problem, to identify optimal configurations of stopover habitats for migratory bird conservation. FLAP is based on a relational network topology model, where stopovers are represented as nodes and potential pathways by links. FLAP finds the optimal migratory pathway that consists of the fewest number of stops in the shortest distance, given that flocks of birds are limited by maximum daily flight capabilities and constrained by carrying capacities of individual habitats. We first present the FLAP model formulation and describe results of an empirical test for a large wetland dataset under different climatic conditions. Our results illustrate how migratory birds might respond to changes in both the temporal and spatial distribution of available stopovers. We also discuss conservation implications for using FLAP to identify priority sites for migratory birds in nature reserve networks. Additionally, the methods presented in this paper may be useful for modelling other types of spatially and temporally dynamic networks. Copyright Taylor & Francis Numéro de notice : A2008-228 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810701674962 En ligne : https://doi.org/10.1080/13658810701674962 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29223 [article]

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Neural network model for standard PCA and its variants applied to remote sensing / S. Chitroub in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 10 (May 2005) . - pp 2197 - 2218 Titre : Neural network model for standard PCA and its variants applied to remote sensing Type de document : Article/Communication Auteurs : S. Chitroub, Auteur Année de publication : 2005 Article en page(s) : pp 2197 - 2218 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] apprentissage automatique [Termes IGN] extraction automatique [Termes IGN] image Landsat-TM [Termes IGN] image multibande [Termes IGN] matrice de covariance [Termes IGN] modèle topologique réseau [Termes IGN] réseau neuronal artificiel [Termes IGN] valeur propre Résumé : (Auteur) The conventional approach for principal component analysis (PCA) and its variants applied to remote sensing involves the computation of the input data covariance/correlation matrix and/or that of noise and application of diagonalization procedures for extracting the eigenvalues and corresponding eigenvectors. When the data dimension grows significantly, the matrix computations and manipulations become practically inefficient and inaccurate due to round-off errors. In addition, all the eigenvalues and their corresponding eigenvectors have to be evaluated. These deficiencies make the conventional scheme inefficient for remote sensing applications. For that we propose here a neural network model that performs the PCA and its variants directly from the original data without any additional non-neuronal computations or preliminary matrix estimation. Since the end user is usually not a neural network specialist, the neural network model as well as its execution are carefully designed in order to be automated as much as possible. This includes both the design of the network topology and the input/output representation as well as the design of the training algorithms. The global convergence of the model is studied. Its application has been realized on Landsat Thematic Mapper (TM) multispectral data. The obtained results show that the model performs well. Numéro de notice : A2005-260 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500075899 En ligne : https://doi.org/10.1080/01431160500075899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27396 [article]

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