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Knowledge-guided consistent correlation analysis of multimode landslide monitoring data / Shuangxi Miao 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 2255 - 2271 Titre : Knowledge-guided consistent correlation analysis of multimode landslide monitoring data Type de document : Article/Communication Auteurs : Shuangxi Miao, Auteur ; Qing Zhu, Auteur ; Bo Zhang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 2255 - 2271 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] base de connaissances [Termes IGN] Chensi (Chine) [Termes IGN] corrélation [Termes IGN] données multisources [Termes IGN] effondrement de terrain [Termes IGN] regroupement de données [Termes IGN] structure géologique [Termes IGN] surveillance géologique Résumé : (Auteur) A novel method called knowledge-guided spatio-temporal consistent correlation analysis (KSTCCA) was developed to discover reliable deformation features induced by multiple factors based on multimode landslide monitoring data. Compared to conventional approaches, KSTCCA integrates both temporal and spatial correlation analysis to improve the consistency of deformation patterns and capture the spatio-temporal heterogeneities in multimode monitoring data. KSTCCA considers both the landslide deformation mechanisms and the relationships between different influential factors as knowledge. Moreover, the method extracts the morphological structures of monitoring curves based on a seven-point approach and identifies knowledge rules using the k-means clustering method. Under the guidance of prior knowledge, a spatial correlation analysis is conducted based on support vector regression, and a temporal correlation analysis of the time lag is carried out based on the morphological structure features. Finally, three kinds of typical monitoring data, including deformation, rainfall, and reservoir water level data collected in the Baishuihe landslide area, China, are used for experimental analysis to verify the validity of the proposed method. Numéro de notice : A2017-700 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1356461 En ligne : https://doi.org/10.1080/13658816.2017.1356461 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88081 [article]

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A geometric correspondence feature based-mismatch removal in vision based-mapping and navigation / Zeyu Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 10 (October 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 10 (October 2017) . - pp 693 - 704 Titre : A geometric correspondence feature based-mismatch removal in vision based-mapping and navigation Type de document : Article/Communication Auteurs : Zeyu Li, Auteur ; Jinling Wang, Auteur ; Charles Toth, Auteur Année de publication : 2017 Article en page(s) : pp 693 - 704 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] appariement de données localisées [Termes IGN] attribut géomètrique [Termes IGN] erreur de positionnement [Termes IGN] regroupement de données [Termes IGN] vision par ordinateur Résumé : (auteur) Images with large-area repetitive texture, significant viewpoint, and illumination changes as well as occlusions often induce high-percentage keypoint mismatches, affecting the performance of vision-based mapping and navigation. Traditional methods for mismatch elimination tend to fail when the percentage of mismatches is high. In order to remove mismatches effectively, a new geometry-based approach is proposed in this paper, where Geometric Correspondence Feature (GCF) is used to represent the tentative correspondence. Based on the clustering property of GCFs from correct matches, a new clustering algorithm is developed to identify the cluster formed by the correct matches. With the defined quality factor calculated from the identified cluster, a Progressive Sample Consensus (PROSAC) process integrated with hyperplane-model is employed to further eliminate mismatches. Extensive experiments based on both simulated and real images in indoor and outdoor environments have demonstrated that the proposed approach can significantly improve the performance of mismatch elimination in the presence of high-percentage mismatches. Numéro de notice : A2017-690 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.14358/PERS.83.10.693 En ligne : https://doi.org/10.14358/PERS.83.10.693 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87856 [article]

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3D building roof reconstruction from airborne LiDAR point clouds : a framework based on a spatial database / Rujun Cao in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 7-8 (July - August 2017) . - pp 1359 - 1380 Titre : 3D building roof reconstruction from airborne LiDAR point clouds : a framework based on a spatial database Type de document : Article/Communication Auteurs : Rujun Cao, Auteur ; Yongjun Zhang, Auteur ; Xinyi Liu, Auteur ; Zongze Zhao, Auteur Année de publication : 2017 Article en page(s) : pp 1359 - 1380 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] base de données localisées [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] niveau de détail [Termes IGN] reconstruction 3D du bâti [Termes IGN] regroupement de données [Termes IGN] semis de points [Termes IGN] toit Résumé : (Auteur) Three-dimensional (3D) building models are essential for 3D Geographic Information Systems and play an important role in various urban management applications. Although several light detection and ranging (LiDAR) data-based reconstruction approaches have made significant advances toward the fully automatic generation of 3D building models, the process is still tedious and time-consuming, especially for massive point clouds. This paper introduces a new framework that utilizes a spatial database to achieve high performance via parallel computation for fully automatic 3D building roof reconstruction from airborne LiDAR data. The framework integrates data-driven and model-driven methods to produce building roof models of the primary structure with detailed features. The framework is composed of five major components: (1) a density-based clustering algorithm to segment individual buildings, (2) an improved boundary-tracing algorithm, (3) a hybrid method for segmenting planar patches that selects seed points in parameter space and grows the regions in spatial space, (4) a boundary regularization approach that considers outliers and (5) a method for reconstructing the topological and geometrical information of building roofs using the intersections of planar patches. The entire process is based on a spatial database, which has the following advantages: (a) managing and querying data efficiently, especially for millions of LiDAR points, (b) utilizing the spatial analysis functions provided by the system, reducing tedious and time-consuming computation, and (c) using parallel computing while reconstructing 3D building roof models, improving performance. Numéro de notice : A2017-305 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1301456 En ligne : http://dx.doi.org/10.1080/13658816.2017.1301456 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85352 [article]

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Graph mapping: Multi-scale community visualization of massive graph data / David Jonker in Information visualization, vol 16 n° 3 (July 2017)  

Public [article]  inInformation visualization > vol 16 n° 3 (July 2017) Titre : Graph mapping: Multi-scale community visualization of massive graph data Type de document : Article/Communication Auteurs : David Jonker, Auteur ; Scott Langevin, Auteur ; David Giesbrecht, Auteur ; et al., Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] graphe [Termes IGN] regroupement de données [Termes IGN] traitement réparti [Termes IGN] visualisation multiéchelle [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Graph visualizations increase the perception of entity relationships in a network. However, as graph size and density increases, readability rapidly diminishes. In this article, we present an end-to-end, tile-based visual analytic approach called graph mapping that utilizes cluster computing to turn large-scale graph (node–link) data into interactive visualizations in modern web browsers. Our approach is designed for end-user analysis of community structure and relationships at macro- and micro scales. We also present the results of several experiments using alternate methods for qualitatively improving comprehensibility of hierarchical community detection visualizations by proposing constraints to state-of-the-art modularity maximization algorithms. Numéro de notice : A2017-758 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1177/1473871616661195 En ligne : https://doi.org/10.1177/1473871616661195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89117 [article]

Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem / Jonathan K. Nelson in Cartography and Geographic Information Science, Vol 44 n° 1 (January 2017)  

Public [article]  inCartography and Geographic Information Science > Vol 44 n° 1 (January 2017) . - pp 35 - 50 Titre : Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem Type de document : Article/Communication Auteurs : Jonathan K. Nelson, Auteur ; Cynthia A. Brewer, Auteur Année de publication : 2017 Article en page(s) : pp 35 - 50 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse diachronique [Termes IGN] exploration de données géographiques [Termes IGN] maladie non infectieuse [Termes IGN] mise à l'échelle [Termes IGN] Pennsylvanie (Etats-Unis) [Termes IGN] problème d'unité zonale modifiable [Termes IGN] regroupement de données Résumé : (auteur) Socioeconomic and health analysts commonly rely on areally aggregated data, in part because government regulations on confidentiality prohibit data release at the individual level. Analytical results from areally aggregated data, however, are sensitive to the modifiable areal unit problem (MAUP). Levels of aggregation as well as the arbitrary and modifiable sizes, shapes, and arrangements of zones affect the validity and reliability of findings from analyses of areally aggregated data. MAUP, long acknowledged, remains unresolved. We present an exploratory spatial data analytical approach (ESDA) to understand the scalar effects of MAUP. To characterize relationships between data aggregation structures and spatial scales, we develop a method for statistically and visually exploring the local indicators of spatial association (LISA) exhibited between a variable and itself across varying levels of aggregation. We demonstrate our approach by analyzing the across-scale relationships of aggregated 2010 median income for the State of Pennsylvania and 2005–2009 cancer diagnosis rates for the State of New York between county–tract, tract–block group, and county–block group level US census designated enumeration units. This method for understanding the relationship between MAUP and spatial scale provides guidance to researchers in selecting the most appropriate scales to aggregate, analyze, and represent data for problem-specific analyses. Numéro de notice : A2017-100 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2015.1093431 En ligne : https://doi.org/10.1080/15230406.2015.1093431 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84479 [article]

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Spatio-temporal analysis of crime by developing a method to detect critical distances for the Knox test / Moshen Kalantari in International journal of geographical information science IJGIS, vol 30 n° 11-12 (November - December 2016)  

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Modeling spatiotemporal information generation / Simon Scheider in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)  

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A hierarchical approach to three-dimensional segmentation of LiDAR data at single-tree level in a multilayered forest / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)  

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Classification 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)  

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Points of interest recommendation from GPS trajectories / Yaqiong Liu in International journal of geographical information science IJGIS, vol 29 n° 6 (June 2015)  

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Co-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data / Xiaojing Wu in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)  

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A polygon-based clustering and analysis framework for mining spatial datasets / Sujing Wang in Geoinformatica, vol 18 n° 3 (July 2014)  

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Band grouping versus band clustering in SVM ensemble classification of hyperspectral imagery / Behnaz Bigdeli in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 6 (June 2013)  

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STHist-C: a highly accurate cluster-based histogram for two and three dimensional geographic data points / Hai Thanh Mai in Geoinformatica, vol 17 n° 2 (April 2013)  

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Processing aggregated data: the location of clusters in health data / Kevin Buchin in Geoinformatica, vol 16 n° 3 (July 2012)  

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