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Parallel computing for fast spatiotemporal weighted regression / Xiang Que in Computers & geosciences, vol 150 (May 2021)
[article]
Titre : Parallel computing for fast spatiotemporal weighted regression Type de document : Article/Communication Auteurs : Xiang Que, Auteur ; Chao Ma, Auteur ; Xiaogang Ma, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104723 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] calcul matriciel
[Termes IGN] étalonnage de modèle
[Termes IGN] modèle de régression
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] régression géographiquement pondérée
[Termes IGN] traitement parallèleRésumé : (auteur) The Spatiotemporal Weighted Regression (STWR) model is an extension of the Geographically Weighted Regression (GWR) model for exploring the heterogeneity of spatiotemporal processes. A key feature of STWR is that it utilizes the data points observed at previous time stages to make better fit and prediction at the latest time stage. Because the temporal bandwidths and a few other parameters need to be optimized in STWR, the model calibration is computationally intensive. In particular, when the data amount is large, the calibration of STWR becomes heavily time-consuming. For example, with 10,000 points in 10 time stages, it takes about 2307 s for a single-core PC to process the calibration of STWR. Both the distance and the weighted matrix in STWR are memory intensive, which may easily cause memory insufficiency as data amount increases. To improve the efficiency of computing, we developed a parallel computing method for STWR by employing the Message Passing Interface (MPI). A cache in the MPI processing approach was proposed for the calibration routine. Also, a matrix splitting strategy was designed to address the problem of memory insufficiency. We named the overall design as Fast STWR (F-STWR). In the experiment, we tested F-STWR in a High-Performance Computing (HPC) environment with a total number of 204,611 observations in 19 years. The results show that F-STWR can significantly improve STWR's capability of processing large-scale spatiotemporal data. Numéro de notice : A2021-300 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article DOI : 10.1016/j.cageo.2021.104723 Date de publication en ligne : 05/03/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104723 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97413
in Computers & geosciences > vol 150 (May 2021) . - n° 104723[article]A Voronoi-based method for land-use optimization using semidefinite programming and gradient descent algorithm / Vorapong Suppakitpaisarn in International journal of geographical information science IJGIS, vol 35 n° 5 (May 2021)
[article]
Titre : A Voronoi-based method for land-use optimization using semidefinite programming and gradient descent algorithm Type de document : Article/Communication Auteurs : Vorapong Suppakitpaisarn, Auteur ; Atthaphon Ariyarit, Auteur ; Supanut Chaidee, Auteur Année de publication : 2021 Article en page(s) : pp 999 - 1031 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] algorithme du gradient
[Termes IGN] algorithme génétique
[Termes IGN] benchmark spatial
[Termes IGN] diagramme de Voronoï
[Termes IGN] mode d'occupation du sol
[Termes IGN] Thaïlande
[Termes IGN] utilisation du solRésumé : (Auteur) The land-use optimization involves divisions of land into subregions to obtain spatial configuration of compact subregions and desired connections among them. Computational geometry-based algorithms, such as Voronoi diagram, are known to be efficient and suitable for iterative design processes to achieve land-use optimization. However, such algorithms assume that generating point positions are given as inputs, while we usually do not know the positions in advance. In this study, we propose a method to automatically calculate the suitable point positions. The method uses (1) semidefinite programming to approximate locations while maintaining relative positions among locations; and (2) gradient descent to iteratively update locations subject to area constraints. We apply the proposed framework to a practical case at Chiang Mai University and compare its performance with a benchmark, the differential genetic algorithm. The results show that the proposed method is 28 times faster than the differential genetic algorithm, while the resulting land allocation error is slightly larger than that of the benchmark but still acceptable. Additionally, the output does not contain disconnected areas, as found in all evolutionary computations, and the compactness is almost equal to the maximum possible value. Numéro de notice : A2021-336 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1841203 Date de publication en ligne : 23/11/2020 En ligne : https://doi.org/10.1080/13658816.2020.1841203 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97555
in International journal of geographical information science IJGIS > vol 35 n° 5 (May 2021) . - pp 999 - 1031[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021051 SL Revue Centre de documentation Revues en salle Disponible Detecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network / Nantheera Anantrasirichai in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
[article]
Titre : Detecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network Type de document : Article/Communication Auteurs : Nantheera Anantrasirichai, Auteur ; Juliet Biggs, Auteur ; Krisztina Kelevitz, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2940 - 2950 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage automatique
[Termes IGN] bati
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] covariance
[Termes IGN] déformation de la croute terrestre
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] effet atmosphérique
[Termes IGN] image radar moirée
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] interpolation spatiale
[Termes IGN] matrice
[Termes IGN] optimisation (mathématiques)
[Termes IGN] représentation parcimonieuse
[Termes IGN] Royaume-Uni
[Termes IGN] zone urbaineRésumé : (auteur) The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of nonexpert stakeholders a challenge. Here, we explore the applicability of deep learning approaches by adapting a pretrained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. For our proof-of-concept, we focus on the U.K. where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides, and tunneling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exist to construct a balanced training data set, and the deformation signals are slower and more localized than in previous applications. We propose three enhancement methods to tackle these problems: 1) spatial interpolation with modified matrix completion; 2) a synthetic training data set based on the characteristics of the real U.K. velocity map; and 3) enhanced overwrapping techniques. Using velocity maps spanning 2015–2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides, and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems. Numéro de notice : A2021-283 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-020-00323-6 Date de publication en ligne : 31/08/2020 En ligne : https://doi.org/10.1007/s12518-020-00323-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97391
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 2940 - 2950[article]Machine learning and geodesy: A survey / Jemil Butt in Journal of applied geodesy, vol 15 n° 2 (April 2021)
[article]
Titre : Machine learning and geodesy: A survey Type de document : Article/Communication Auteurs : Jemil Butt, Auteur ; Andreas Wieser, Auteur ; Zan Gojcic, Auteur ; Caifa Zhou, Auteur Année de publication : 2021 Article en page(s) : pp 117 - 133 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse de données
[Termes IGN] apprentissage automatique
[Termes IGN] données géodésiques
[Termes IGN] espace de Hilbert
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) The goal of classical geodetic data analysis is often to estimate distributional parameters like expected values and variances based on measurements that are subject to uncertainty due to unpredictable environmental effects and instrument specific noise. Its traditional roots and focus on analytical solutions at times require strong prior assumptions regarding problem specification and underlying probability distributions that preclude successful application in practical cases for which the goal is not regression in presence of Gaussian noise. Machine learning methods are more flexible with respect to assumed regularity of the input and the form of the desired outputs and allow for nonparametric stochastic models at the cost of substituting easily analyzable closed form solutions by numerical schemes. This article aims at examining common grounds of geodetic data analysis and machine learning and showcases applications of algorithms for supervised and unsupervised learning to tasks concerned with optimal estimation, signal separation, danger assessment and design of measurement strategies that occur frequently and naturally in geodesy. Numéro de notice : A2021-321 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0043 Date de publication en ligne : 20/02/2021 En ligne : https://doi.org/10.1515/jag-2020-0043 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97478
in Journal of applied geodesy > vol 15 n° 2 (April 2021) . - pp 117 - 133[article]Study on offshore seabed sediment classification based on particle size parameters using XGBoost algorithm / Fengfan Wang in Computers & geosciences, vol 149 (April 2021)
[article]
Titre : Study on offshore seabed sediment classification based on particle size parameters using XGBoost algorithm Type de document : Article/Communication Auteurs : Fengfan Wang, Auteur ; Jia Yu, Auteur ; Zhijie Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104713 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatiale
[Termes IGN] calcul matriciel
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] diagramme
[Termes IGN] échantillon
[Termes IGN] Extreme Gradient Machine
[Termes IGN] fond marin
[Termes IGN] gravier
[Termes IGN] image à haute résolution
[Termes IGN] sédimentRésumé : (auteur) Folk's textual classification scheme which is widely used for sediment study operates with the proportions of gravel, sand, silt and clay fractions conventionally. However, dealing with data from different sources usually needs to face missing values that may make the classification difficult. To solve this problem and discover other methods of analyzing the scheme, with samples of offshore seabed sediment, a two-stage model was established to predict a sample's class using the XGBoost algorithm as well as the grain size parameters as input features. The final model was evaluated with quantitative performance measures of recall, precision and F1 score, and by comparing sediment texture maps using the predicted and the actual data. The results show that the model performs well on extraction of sediment samples without gravel fraction, and prediction of classes that have independent characteristics of grain size parameters or samples not near the boundaries of classes in the ternary diagram. The predicted sediment texture is close to the actual and could be reliable due to errors with little impact on further applications. It is demonstrated that the model could be an auxiliary or alternative approach to offshore sediment texture mapping, as well as supplementary to the analysis of sedimentary environment. Numéro de notice : A2021-289 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104713 Date de publication en ligne : 12/02/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104713 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97400
in Computers & geosciences > vol 149 (April 2021) . - n° 104713[article]Utilizing urban geospatial data to understand heritage attractiveness in Amsterdam / Sevim Sezi Karayazi in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)PermalinkAggregating land-use polygons considering line features as separating map elements / Sven Gedicke in Cartography and Geographic Information Science, vol 48 n° 2 (March 2021)PermalinkCharacterizing urban land changes of 30 global megacities using nighttime light time series stacks / Qiming Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)PermalinkCompressive Sensing appliqué au traitement de données InSAR pour le suivi de la déformation des zones urbaines / Matthieu Rebmeister in XYZ, n° 166 (mars 2021)PermalinkGeographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships / Sensen Wu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)PermalinkMinimum-error world map projections defined by polydimensional meshes / Justin H. Kunimune in International journal of cartography, vol 7 n° 1 (March 2021)PermalinkON GLONASS pseudo-range inter-frequency bias solution with ionospheric delay modeling and the undifferenced uncombined PPP / Zheng Zhang in Journal of geodesy, vol 95 n° 3 (March 2021)PermalinkAgricultural land partitioning model based on irrigation efficiency using a multi‐objective artificial bee colony algorithm / Mehrdad Bijandi in Transactions in GIS, Vol 25 n° 1 (February 2021)PermalinkAn improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments / Zohreh Masoumi in Geocarto international, vol 36 n° 2 ([01/02/2021])PermalinkAssessment of mass-induced sea level variability in the Tropical Indian Ocean based on GRACE and altimeter observations / Shiva Shankar Manche in Journal of geodesy, vol 95 n° 2 (February 2021)Permalink