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Comparison of atmospheric mass density models using a new data source: COSMIC satellite ephemerides / Yang Yang in IEEE Aerospace and Electronic Systems Magazine, vol 37 n° 2 (February 2022)  

Public [article]  inIEEE Aerospace and Electronic Systems Magazine > vol 37 n° 2 (February 2022) . - pp 6 - 22 Titre : Comparison of atmospheric mass density models using a new data source: COSMIC satellite ephemerides Type de document : Article/Communication Auteurs : Yang Yang, Auteur ; Ronald Maj, Auteur ; Changyong He , Auteur ; Robert Norman, Auteur ; Emma Kerr, Auteur ; Brett Anthony Carter, Auteur ; Julie Louise Currie, Auteur ; Steve Gower, Auteur Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : pp 6 - 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] analyse comparative [Termes IGN] atmosphère terrestre [Termes IGN] éphémérides de satellite [Termes IGN] International Reference Ionosphere [Termes IGN] masse d'air [Termes IGN] modèle atmosphérique [Termes IGN] orbite basse [Termes IGN] teneur totale en électrons Résumé : (auteur) Atmospheric mass density (AMD) plays a vital role in the drag calculation for space objects in low Earth orbit. Many empirical AMD models have been developed and used for orbit prediction and efforts continue to improve their accuracy in forecasting high-altitude atmospheric conditions. Previous studies have assessed these models at the height of 200 km to 600 km. In this paper, four state-of-the-art AMD models, i.e., MSISE90, MSISE00, JB2008 and DTM2013 are assessed for their orbit prediction (OP) capabilities by using a new data source of COSMIC satellite ephemerides at an orbital height of ~800 km, where the contribution of ions in the total AMD is more significant. A new testing model was developed by accounting for ion contribution based on the International Reference Ionosphere 2016 model, including many more ion species that are not accounted for in other AMD models. In the assessment, two periods of forty days were chosen in 2014-2015 and 2018-2019, representing solar maximum and minimum periods, respectively, to assess four existing AMD models and the proposed model. Thorough analyses were conducted to compare OP results using different AMD models with precise reference ephemerides of COSMIC satellites and based on various space weather indices. It is shown that the proposed model outperforms all other AMD models in terms of OP errors during the solar maximum period. During solar minimum, the drag acceleration is relatively small for COSMIC satellites. Assessment of all AMD models in the orbit prediction process tends to be contaminated by the remaining uncertainty sources, such as solar radiation pressure. Numéro de notice : A2022-070 Affiliation des auteurs : ENSG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/MAES.2021.3125101 Date de publication en ligne : 20/12/2021 En ligne : https://doi.org/10.1109/MAES.2021.3125101 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99376 [article]

Voir aussi

• Comparison of atmospheric mass density models using a new data source: COSMIC satellite ephemerides / Yang Yang (2020)  

Exploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: a comparative study of four methods / Bin Zhang in GIScience and remote sensing, vol 59 n° 1 (2022)  

Public [article]  inGIScience and remote sensing > vol 59 n° 1 (2022) . - pp 71 - 95 Titre : Exploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: a comparative study of four methods Type de document : Article/Communication Auteurs : Bin Zhang, Auteur ; Haijun Wang, Auteur Année de publication : 2022 Article en page(s) : pp 71 - 95 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] apprentissage automatique [Termes IGN] automate cellulaire [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] croissance urbaine [Termes IGN] entropie maximale [Termes IGN] modèle de simulation [Termes IGN] paysage urbain [Termes IGN] Pékin (Chine) [Termes IGN] régression logistique [Termes IGN] réseau neuronal artificiel [Termes IGN] urbanisation [Termes IGN] Wuhan (Chine) Résumé : (auteur) As a powerful predictive technique based on machine learning, the maximum entropy (MaxEnt) model has been widely used in geographic modeling. However, its performance in calibrating cellular automata (CA) for urban growth simulation has not been investigated. This study compares the MaxEnt model with logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM) models to explore its advantages in simulating urban growth and interpreting driving mechanisms. With the land use data of 2000 and 2020 from GlobeLand30, the constructed LR-CA, ANN-CA, SVM-CA, and MaxEnt-CA models are applied to simulate the urban growth of Beijing, Tianjin, and Wuhan, respectively. Their performance has been evaluated from multiple aspects such as the accuracy of training, testing, and projecting, computational efficiency, simulation accuracy, and simulated urban landscape. The results indicate that the MaxEnt model is superior to the other models except for the computational efficiency, but the time required for the MaxEnt training and projecting is acceptable and far less than that of the SVM. Taking the LR-CA as the benchmark, the kappa coefficients (Kappa) of the MaxEnt-CA have been increased by 4.20%, 3.38%, and 5.87% in Beijing, Tianjin, and Wuhan, respectively; the increments of corresponding figure of merits (FoM) are 6.26%, 4.58%, and 8.49%. The driving mechanisms of urban growth such as the interactions, response curves, and importance of spatial variables, have also been revealed by the MaxEnt modeling. The driving mechanisms of urban growth in Tianjin are more complex than that in Beijing and Wuhan, because there are more variable interactions; the relationships between spatial factors and urban growth in the three study areas are all nonlinear; the topographic factors and city center of Beijing, the traffic factors and water bodies of Tianjin, and the traffic factors, city center and water bodies of Wuhan are significant factors affecting their urban growth. Numéro de notice : A2022-130 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1080/15481603.2021.2016240 Date de publication en ligne : 30/12/2021 En ligne : https://doi.org/10.1080/15481603.2021.2016240 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99715 [article]

A geographically weighted artificial neural network / Julian Haguenauer in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 2 (February 2022) . - pp 215 - 235 Titre : A geographically weighted artificial neural network Type de document : Article/Communication Auteurs : Julian Haguenauer, Auteur ; Marco Helbich, Auteur Année de publication : 2022 Article en page(s) : pp 215 - 235 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] analyse de sensibilité [Termes IGN] Autriche [Termes IGN] coût [Termes IGN] évaluation foncière [Termes IGN] hétérogénéité spatiale [Termes IGN] logement [Termes IGN] régression géographiquement pondérée [Termes IGN] relation spatiale [Termes IGN] réseau neuronal artificiel Résumé : (auteur) While recent developments have extended geographically weighted regression (GWR) in many directions, it is usually assumed that the relationships between the dependent and the independent variables are linear. In practice, however, it is often the case that variables are nonlinearly associated. To address this issue, we propose a geographically weighted artificial neural network (GWANN). GWANN combines geographical weighting with artificial neural networks, which are able to learn complex nonlinear relationships in a data-driven manner without assumptions. Using synthetic data with known spatial characteristics and a real-world case study, we compared GWANN with GWR. While the results for the synthetic data show that GWANN performs better than GWR when the relationships within the data are nonlinear and their spatial variance is high, the results based on the real-world data demonstrate that the performance of GWANN can also be superior in a practical setting. Numéro de notice : A2022-162 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1871618 Date de publication en ligne : 08/02/2021 En ligne : https://doi.org/10.1080/13658816.2021.1871618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99785 [article]

Spatiotemporal temperature fusion based on a deep convolutional network / Xuehan Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 2 (February 2022) . - pp 93 - 101 Titre : Spatiotemporal temperature fusion based on a deep convolutional network Type de document : Article/Communication Auteurs : Xuehan Wang, Auteur ; Zhenfeng Shao, Auteur ; Xiao Huang, Auteur ; Deren Li, Auteur Année de publication : 2022 Article en page(s) : pp 93 - 101 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] apprentissage profond [Termes IGN] Chine [Termes IGN] données spatiotemporelles [Termes IGN] fusion de données multisource [Termes IGN] image Landsat [Termes IGN] image Terra-MODIS [Termes IGN] réseau neuronal convolutif [Termes IGN] série temporelle [Termes IGN] température au sol [Termes IGN] température de surface Résumé : (Auteur) High-spatiotemporal-resolution land surface temperature (LST) images are essential in various fields of study. However, due to technical constraints, sensing systems have difficulty in providing LSTs with both high spatial and high temporal resolution. In this study, we propose a multi-scale spatiotemporal temperature-image fusion network (MSTTIFN) to generate high-spatial-resolution LST products. The MSTTIFN builds nonlinear mappings between the input Moderate Resolution Imaging Spectroradiometer (MODIS) LSTs and the out- put Landsat LSTs at the target date with two pairs of references and therefore enhances the resolution of time-series LSTs. We conduct experiments on the actual Landsat and MODIS data in two study areas (Beijing and Shandong) and compare our proposed MSTTIFN with four competing methods: the Spatial and Temporal Adaptive Reflectance Fusion Model, the Flexible Spatiotemporal Data Fusion Model, a two-stream convolutional neural network (StfNet), and a deep learning-based spatiotemporal temperature-fusion network. Results reveal that the MSTTIFN achieves the best and most stable performance. Numéro de notice : A2022-064 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00023R2 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.14358/PERS.21-00023R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99724 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2022021	SL	Revue	Centre de documentation	Revues en salle	Disponible

Three-Dimensional point cloud analysis for building seismic damage information / Fan Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 2 (February 2022) . - pp 103 - 111 Titre : Three-Dimensional point cloud analysis for building seismic damage information Type de document : Article/Communication Auteurs : Fan Yang, Auteur ; Zhiwei Fan, Auteur ; Chao Wen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 103 - 111 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] analyse de groupement [Termes IGN] analyse en composantes principales [Termes IGN] densité des points [Termes IGN] détection du bâti [Termes IGN] dommage matériel [Termes IGN] données localisées 3D [Termes IGN] extraction de données [Termes IGN] filtrage de points [Termes IGN] mur [Termes IGN] séisme [Termes IGN] semis de points Résumé : (Auteur) Postearthquake building damage assessment requires professional judgment; however, there are factors such as high workload and human error. Making use of Terrestrial Laser Scanning data, this paper presents a method for seismic damage information extraction. This new method is based on principal component analysis calculating the local surface curvature of each point in the point cloud. Then use the nearest point angle algorithm, combined with the data features of the actual measured value to identify point cloud seismic information, and filter the points that tend to the plane by setting the threshold value. Based on the statistical analysis of the normal vector, the raw point cloud data are deplanarized to obtain the preliminary results of seismic damage information. The density clustering algorithm is used to denoise the initially extracted seismic damage information. Ultimately, we can obtain the distribution patterns and characteristics of cracks in the walls of the building. The extraction result of the seismic damage information point cloud data is compared with the photos collected at the site, showing that the algorithm steps successfully identify the crack and shed wall skin information recorded in the site photos (identification rate: 95%). Point cloud distribution maps of cracked and shed siding areas determine quantitative information on seismic damage, providing a higher level of performance and detail than direct contact measurements. Numéro de notice : A2022-065 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00019R3 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.14358/PERS.21-00019R3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99727 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2022021	SL	Revue	Centre de documentation	Revues en salle	Disponible

Using vertices of a triangular irregular network to calculate slope and aspect / Guanghui Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)  

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3D stem modelling in tropical forest: towards improved biomass and biomass change estimates / Sébastien Bauwens (2022)  

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Apport des nouveaux systèmes GNSS de cartographie du niveau marin à l’exploitation des données altimétriques en zone côtière / Clémence Chupin (2022)  

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Best integer equivariant position estimation for multi-GNSS RTK: a multivariate normal and t-distributed performance comparison / Robert Odolinski in Journal of geodesy, vol 96 n° 1 (January 2022)  

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A comparison of linear-mode and single-photon airborne LiDAR in species-specific forest inventories / Janne Raty in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)  

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A comprehensive assessment of four-satellite QZSS constellation: navigation signals, broadcast ephemeris, availability, SPP, interoperability with GPS, and ISB against GPS / Xuanping Li in Survey review, vol 54 n° 382 (January 2022)  

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Deep image translation with an affinity-based change prior for unsupervised multimodal change detection / Luigi Tommaso Luppino in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)  

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Détection des micro et macroplastiques à partir de mesures spectrales / Martin Cubaud (2022) 

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Developing the potential of airborne lidar systems for the sustainable management of forests / Karun Dayal (2022)  

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Empirical comparison between stochastic and deterministic modifiers over the French Auvergne geoid computation test-bed / Ropesh Goyal in Survey review, vol 54 n° 382 (January 2022)  

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A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods / Pengxiang Zhao in Remote sensing, vol 14 n° 1 (January-1 2022)  

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Improving urban land cover mapping with the fusion of optical and SAR data based on feature selection strategy / Qing Ding in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 1 (January 2022)  

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Landslide evolution pattern revealed by multi-temporal DSMs obtained from historical aerial images / Michele Santangelo (2022)  

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Numérique versus symbolique : dialogue ontologique entre deux approches / Hélène Mathian in Revue internationale de géomatique, vol 31 n° 1-2 (janvier - juin 2022)  

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Towards synthetic sensing for smart cities : a machine/deep learning-based approach / Faraz Malik Awan (2022)  

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UAVs for the environmental sciences / Annette Eltner (2022)  

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Adaptive feature weighted fusion nested U-Net with discrete wavelet transform for change detection of high-resolution remote sensing images / Congcong Wang in Remote sensing, vol 13 n° 24 (December-2 2021)  

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Building fuzzy areal geographical objects from point sets / Jifa Guo in Transactions in GIS, vol 25 n° 6 (December 2021)  

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Comparative analysis for methods of building digital elevation models from topographic maps using geoinformation technologies / Vadim Belenok in Geodesy and cartography, vol 47 n° 4 (December 2021)  

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Evaluating narrative in geoportals for territorial public policies / Luis Manuel Batista in Cartographica, vol 56 n° 4 (Winter 2021)  

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