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Pattern-based identification and mapping of landscape types using multi-thematic data / Jakub Nowosad in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 8 (August 2021) . - pp 1634 - 1649 Titre : Pattern-based identification and mapping of landscape types using multi-thematic data Type de document : Article/Communication Auteurs : Jakub Nowosad, Auteur ; Tomasz F. Stepinski, Auteur Année de publication : 2021 Article en page(s) : pp 1634 - 1649 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] gestion des ressources [Termes IGN] gestion foncière [Termes IGN] matrice de co-occurrence [Termes IGN] modèle mathématique [Termes IGN] modélisation spatiale [Termes IGN] occupation du sol [Termes IGN] paysage [Termes IGN] régionalisation (segmentation) [Termes IGN] regroupement de données [Termes IGN] segmentation en régions Résumé : (auteur) Categorical maps of landscape types (LTs) are useful abstractions that simplify spatial and thematic complexity of natural landscapes, thus facilitating land resources management. A local landscape arises from a fusion of patterns of natural themes (such as land cover, landforms, etc.), which makes an unsupervised identification and mapping of LTs difficult. This paper introduces the integrated co-occurrence matrix (INCOMA) – a signature for numerical representation of multi-thematic categorical patterns. INCOMA enables an unsupervised identification and mapping of LTs. The region is tessellated into a large number of local landscapes – patterns of themes over small square-shaped neighborhoods. With local landscapes represented by INCOMA signatures and with dissimilarities between local landscapes calculated using the Jensen-Shannon Divergence (JSD), LTs can be identified and mapped using standard clustering or segmentation techniques. Resultant LTs are typically heterogeneous with respect to categories of contributing themes reflecting the human perception of a landscape. LTs calculated by INCOMA are more faithful abstractions of actual landscapes than LTs obtained by the current method of choice – the map overlay. The concept of INCOMA is described, and its application is demonstrated by an unsupervised mapping of LT zones in Europe based on combined patterns of land cover and landforms. Numéro de notice : A2021-549 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1893324 Date de publication en ligne : 02/03/2021 En ligne : https://doi.org/10.1080/13658816.2021.1893324 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98064 [article]

Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks / Mahmoud Saeedimoghaddam in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 5 (May 2020) . - pp 947 - 968 Titre : Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks Type de document : Article/Communication Auteurs : Mahmoud Saeedimoghaddam, Auteur ; Tomasz F. Stepinski, Auteur Année de publication : 2020 Article en page(s) : pp 947 - 968 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] carrefour [Termes IGN] carte ancienne [Termes IGN] carte numérisée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] données localisées [Termes IGN] Etats-Unis [Termes IGN] extraction du réseau routier [Termes IGN] image RVB [Termes IGN] numérisation automatique [Termes IGN] représentation cartographique [Termes IGN] système d'information géographique [Termes IGN] vision par ordinateur Résumé : (auteur) Road intersection data have been used across a range of geospatial analyses. However, many datasets dating from before the advent of GIS are only available as historical printed maps. To be analyzed by GIS software, they need to be scanned and transformed into a usable (vector-based) format. Because the number of scanned historical maps is voluminous, automated methods of digitization and transformation are needed. Frequently, these processes are based on computer vision algorithms. However, the key challenges to this are (1) the low conversion accuracy for low quality and visually complex maps, and (2) the selection of optimal parameters. In this paper, we used a region-based deep convolutional neural network-based framework (RCNN) for object detection, in order to automatically identify road intersections in historical maps of several cities in the United States of America. We found that the RCNN approach is more accurate than traditional computer vision algorithms for double-line cartographic representation of the roads, though its accuracy does not surpass all traditional methods used for single-line symbols. The results suggest that the number of errors in the outputs is sensitive to complexity and blurriness of the maps, and to the number of distinct red-green-blue (RGB) combinations within them. Numéro de notice : A2020-205 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1696968 Date de publication en ligne : 28/11/2019 En ligne : https://doi.org/10.1080/13658816.2019.1696968 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94882 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2020051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Spatial association between regionalizations using the information-theoretical V-measure / Jakub Nowosad in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 11-12 (November - December 2018) . - pp 2386 - 2401 Titre : Spatial association between regionalizations using the information-theoretical V-measure Type de document : Article/Communication Auteurs : Jakub Nowosad, Auteur ; Tomasz F. Stepinski, Auteur Année de publication : 2018 Article en page(s) : pp 2386 - 2401 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse de variance [Termes IGN] distribution spatiale [Termes IGN] logiciel libre [Termes IGN] régionalisation (segmentation) [Termes IGN] variable Résumé : (Auteur) There is a keen interest in calculating spatial associations between two variables spanning the same study area. Many methods for calculating such associations have been proposed, but the case when both variables are categorical is underdeveloped despite the fact that many datasets of interest are in the form of either regionalizations or thematic maps. In this paper, we advance this case by adapting the so-called -measure method from its original information-theoretical formulation to the analysis of variance formulation which provides more insight for spatial analysis. We present a step-by-step derivation of the -measure from the perspective of the analysis of variance. The method produces three indices of global association and two sets of local association indicators which could be mapped to indicate spatial distribution of association strength. The open-source software for calculating all indices from vector datasets accompanies the paper. To showcase the utility of the -measure, we identified three different application contexts: comparative, associative, and derivative, and present an example of each of them. The -measure method has several advantages over the widely used Mapcurves method, it has clear interpretations in terms of mutual information as well as in terms of analysis of variance, it provides more precise assessment of association, it is ready-to-use through the accompanying software, and the examples given in the paper serves as a guide to the gamut of its possible applications. Two specific contributions stemming from our re-analysis of the -measure are the finding of the conceptual flaw in the Geographical Detector—a method to quantify associations between numerical and categorical spatial variables, and a proposal for the new, cartographically based algorithm for finding an optimal number of regions in clustering-derived regionalizations. Numéro de notice : A2018-526 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1511794 Date de publication en ligne : 30/08/2018 En ligne : https://doi.org/10.1080/13658816.2018.1511794 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91353 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2018061	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Controlling patterns of geospatial phenomena / Tomasz F. Stepinski in Geoinformatica, vol 15 n° 3 (July 2011)  

Public [article]  inGeoinformatica > vol 15 n° 3 (July 2011) . - pp 399 - 416 Titre : Controlling patterns of geospatial phenomena Type de document : Article/Communication Auteurs : Tomasz F. Stepinski, Auteur ; W. Ding, Auteur ; C. Eick, Auteur Année de publication : 2011 Article en page(s) : pp 399 - 416 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatiale [Termes IGN] exploration de données géographiques [Termes IGN] phénomène géographique [Termes IGN] relation topologique Résumé : (Auteur) Modeling spatially distributed phenomena in terms of its controlling factors is a recurring problem in geoscience. Most efforts concentrate on predicting the value of response variable in terms of controlling variables either through a physical model or a regression model. However, many geospatial systems comprises complex, nonlinear, and spatially non-uniform relationships, making it difficult to even formulate a viable model. This paper focuses on spatial partitioning of controlling variables that are attributed to a particular range of a response variable. Thus, the presented method surveys spatially distributed relationships between predictors and response. The method is based on association analysis technique of identifying emerging patterns, which are extended in order to be applied more effectively to geospatial data sets. The outcome of the method is a list of spatial footprints, each characterized by a unique “controlling pattern”—a list of specific values of predictors that locally correlate with a specified value of response variable. Mapping the controlling footprints reveals geographic regionalization of relationship between predictors and response. The data mining underpinnings of the method are given and its application to a real world problem is demonstrated using an expository example focusing on determining variety of environmental associations of high vegetation density across the continental United States. Numéro de notice : A2011-206 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-010-0107-2 Date de publication en ligne : 29/01/2010 En ligne : https://doi.org/10.1007/s10707-010-0107-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30984 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-2011031	RAB	Revue	Centre de documentation	En réserve L003	Disponible