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Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena Moll in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
[article]
Titre : Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis Type de document : Article/Communication Auteurs : Marta Sapena Moll, Auteur ; Luis Angel Ruiz, Auteur Année de publication : 2021 Article en page(s) : pp 375 - 396 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse discriminante
[Termes IGN] analyse spatio-temporelle
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte d'utilisation du sol
[Termes IGN] changement d'occupation du sol
[Termes IGN] croissance urbaine
[Termes IGN] distance euclidienne
[Termes IGN] modèle de croissance végétale
[Termes IGN] pondérationRésumé : (auteur) The spatial pattern of urban growth determines how the physical, socio-economic and environmental characteristics of urban areas change over time. Monitoring urban areas for early identification of spatial patterns facilitates assuring their sustainable growth. In this paper, we assess the use of spatio-temporal metrics from land-use/land-cover (LULC) maps to identify growth patterns. We applied LULC change models to simulate different scenarios of urban growth spatial patterns (i.e., expansion, compact, dispersed, road-based and leapfrog) on various baseline urban forms (i.e., monocentric, polycentric, sprawl and linear). Then, we computed the spatio-temporal metrics for the simulated scenarios, selected the most informative metrics by applying discriminant analysis and classified the growth patterns using clustering methods. Two metrics, Weighted mean expansion and Weighted Euclidean distance, which account for the densification, compactness and concentration of urban growth, were the most efficient for classifying the five growth patterns, despite the influence of the baseline urban form. These metrics have the potential to identify growth patterns for monitoring and evaluating the management of developing urban areas. Numéro de notice : A2021-040 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1817463 Date de publication en ligne : 08/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1817463 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96752
in International journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 375 - 396[article]A points of interest matching method using a multivariate weighting function with gradient descent optimization / Zhou Yang in Transactions in GIS, Vol 25 n° 1 (February 2021)
[article]
Titre : A points of interest matching method using a multivariate weighting function with gradient descent optimization Type de document : Article/Communication Auteurs : Zhou Yang, Auteur ; Mingjun Wang, Auteur ; Chen Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 359 - 381 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] algorithme du gradient
[Termes IGN] appariement automatique
[Termes IGN] appariement de données localisées
[Termes IGN] apprentissage automatique
[Termes IGN] données localisées des bénévoles
[Termes IGN] données multisources
[Termes IGN] exploration de données
[Termes IGN] intégration de données
[Termes IGN] point d'intérêt
[Termes IGN] pondération
[Termes IGN] qualité des donnéesRésumé : (Auteur) Volunteered geographic information contains abundant valuable data, which can be applied to various spatiotemporal geographical analyses. While the useful information may be distributed in different, low‐quality data sources, this issue can be solved by data integration. Generally, the primary task of integration is data matching. Unfortunately, due to the complexity and irregularities of multi‐source data, existing studies have found it difficult to efficiently establish the correspondence between different sources. Therefore, we present a multi‐stage method to match multi‐source data using points of interest. A spatial filter is constructed to obtain candidate sets for geographical entities. The weights of non‐spatial characteristics are examined by a machine learning‐related algorithm with artificially labeled random samples. A case study on Fuzhou reveals that an average of 95% of instances are accurately matched. Thus, our study provides a novel solution for researchers who are engaged in data mining and related work to accurately match multi‐source data via knowledge obtained by the idea and methods of machine learning. Numéro de notice : A2021-189 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12690 Date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.1111/tgis.12690 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97158
in Transactions in GIS > Vol 25 n° 1 (February 2021) . - pp 359 - 381[article]Determination of precise Galileo orbits using combined GNSS and SLR observations / Grzegorz Bury in GPS solutions, vol 25 n° 1 (January 2021)
[article]
Titre : Determination of precise Galileo orbits using combined GNSS and SLR observations Type de document : Article/Communication Auteurs : Grzegorz Bury, Auteur ; Krzysztof Sosnica, Auteur ; Radoslaw Zajdel, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 11 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Techniques orbitales
[Termes IGN] données GNSS
[Termes IGN] données TLS (télémétrie)
[Termes IGN] Galileo
[Termes IGN] International Terrestrial Reference Frame
[Termes IGN] orbite précise
[Termes IGN] orbitographie
[Termes IGN] pondérationRésumé : (auteur) Galileo satellites are equipped with laser retroreflector arrays for satellite laser ranging (SLR). In this study, we develop a methodology for the GNSS-SLR combination at the normal equation level with three different weighting strategies and evaluate the impact of laser observations on the determined Galileo orbits. We provide the optimum weighting scheme for precise orbit determination employing the co-location onboard Galileo. The combined GNSS-SLR solution diminishes the semimajor axis formal error by up to 62%, as well as reduces the dependency between values of formal errors and the elevation of the Sun above the orbital plane—the β angle. In the combined solution, the standard deviation of the SLR residuals decreases from 36.1 to 29.6 mm for Galileo-IOV satellites and |β|> 60°, when compared to GNSS-only solutions. Moreover, the bias of the Length-of-Day parameter is 20% lower for the combined solution when compared to the microwave one. As a result, the combination of GNSS and SLR observations provides promising results for future co-locations onboard the Galileo satellites for the orbit determination, realization of the terrestrial reference frames, and deriving geodetic parameters. Numéro de notice : A2021-008 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01045-3 Date de publication en ligne : 31/10/2020 En ligne : https://doi.org/10.1007/s10291-020-01045-3 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96298
in GPS solutions > vol 25 n° 1 (January 2021) . - n° 11[article]Evaluation of a neural network with uncertainty for detection of ice and water in SAR imagery / Nazanin Asadi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
[article]
Titre : Evaluation of a neural network with uncertainty for detection of ice and water in SAR imagery Type de document : Article/Communication Auteurs : Nazanin Asadi, Auteur ; K. Andrea Scott, Auteur ; Alexander S. Komarov, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 247 - 259 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] assimilation des données
[Termes IGN] classification pixellaire
[Termes IGN] glace de mer
[Termes IGN] image radar moirée
[Termes IGN] incertitude des données
[Termes IGN] modèle d'incertitude
[Termes IGN] Perceptron multicouche
[Termes IGN] pondération
[Termes IGN] précision de la classification
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificielRésumé : (auteur) Synthetic aperture radar (SAR) sea ice imagery is a promising source of data for sea ice data assimilation. Classification of SAR sea ice imagery into ice and water is of particular relevance due to its relationship with ice concentration, a key variable in sea ice data assimilation systems. With increasing volumes of SAR data, automated methods to carry out these classifications are of particular importance. Although several automated approaches have been proposed, none look at the impact of including an estimate of uncertainty of the model parameters and input features on the classification output. This article uses an established database of SAR image features to train a multilayer perceptron (MLP) neural network to classify pixel locations as either ice, water, or unknown. The classification accuracies are benchmarked using a recently developed logistic regression approach for the same database. The two methods are found to be comparable. The MLP approach is then enhanced to allow uncertainty to be estimated at each pixel location. Following methods proposed in the deep learning community, two kinds of uncertainty are considered. The first, epistemic uncertainty, is that due to uncertainty in the MLP weights. The second kind of uncertainty, aleatoric uncertainty, is that which cannot be explained by the model, and is therefore associated with the input data. It is found that including these uncertainties in the MLP models reduces their accuracies slightly, but also reduces misclassification rates. This is of particular importance for data assimilation applications, where misclassifications could severely degrade the analysis. Numéro de notice : A2021-033 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2992454 Date de publication en ligne : 09/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2992454 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96735
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 247 - 259[article]Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation / George Grekousis in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)
[article]
Titre : Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation Type de document : Article/Communication Auteurs : George Grekousis, Auteur Année de publication : 2021 Article en page(s) : pp 152 - 174 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] classification floue
[Termes IGN] données démographiques
[Termes IGN] New York (Etats-Unis ; ville)
[Termes IGN] optimisation par essaim de particules
[Termes IGN] pondération
[Termes IGN] régression géographiquement pondérée
[Termes IGN] santé
[Termes IGN] segmentation
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) Fuzzy geographically weighted clustering has been proposed as an approach for improving fuzzy c-means algorithm when applied to geodemographic analysis. This clustering method allows a spatial entity to belong to more than one cluster with varying degrees, namely, membership values. Although fuzzy geographically weighted clustering attempts to create geographically aware clusters, it partially fails to trace spatial dependence and heterogeneity because, as a global metric, the membership values are calculated across the entire set of spatial entities. Here we introduce the first local version of fuzzy geographically weighted clustering, ‘local fuzzy geographically weighted clustering.’ In local fuzzy geographically weighted clustering, the membership values of a spatial entity are updated only according to the membership values of the spatial entities within its neighborhood and not across the entire set of entities, as originally proposed by the global metric. Additionally, we apply particle swarm optimization meta-heuristic to overcome the random initialization problem regarding the fuzzy c-means algorithm. To evaluate our method we compare local fuzzy geographically weighted clustering to global fuzzy geographically weighted clustering using a cancer incident benchmark dataset for Manhattan, New York. The results show that local fuzzy geographically weighted clustering outperforms the global version in all experimental settings. Numéro de notice : A2021-022 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808221 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808221 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96525
in International journal of geographical information science IJGIS > vol 35 n° 1 (January 2021) . - pp 152 - 174[article]Réservation
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