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Termes IGN > mathématiques > statistique mathématique
statistique mathématique
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biométrie,
échantillonnage (statistique), probabilité, statistique. >>Terme(s) spécifique(s) : analyse de régression, analyse de variance, analyse des données, analyse multivariée, analyse séquentielle, calcul d'erreur, carré latin, corrélation (statistique), efficacité asymptotique (statistique), fonction pseudo-aléatoire, loi des grands nombres, modèle linéaire (statistique), modèle non linéaire (statistique), moindre carré, physique statistique, plan d'expérience, rang et sélection (statistique), rupture (statistique), SAS (logiciel), série chronologique, statistique non paramétrique, statistique robuste, tableau de contingence, test d'hypothèses (statistique), statistique stellaire. Equiv. LCSH : Mathematical statistics. Domaine(s) : 510. |
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HiPerMovelets: high-performance movelet extraction for trajectory classification / Tarlis Tortelli Portela in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)
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[article]
Titre : HiPerMovelets: high-performance movelet extraction for trajectory classification Type de document : Article/Communication Auteurs : Tarlis Tortelli Portela, Auteur ; Jonata Tyska Carvalho, Auteur ; Vania Bogorny, Auteur Année de publication : 2022 Article en page(s) : pp 1012 - 1036 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] classification
[Termes IGN] exploration de données géographiques
[Termes IGN] jeu de données localisées
[Termes IGN] trace numérique
[Termes IGN] trajet (mobilité)Résumé : (auteur) In the last decade, trajectory classification has received significant attention. The vast amount of data generated on social media, the use of sensor networks, IOT devices and other Internet-enabled sources allowed the semantic enrichment of mobility data, making the classification task more challenging. Existing trajectory classification methods have mainly considered space, time and numerical data, ignoring the semantic dimensions. Only recently proposed methods as Movelets and MASTERMovelets can handle all types of dimensions. MASTERMovelets is the only method that automatically discovers the best dimension combination and subtrajectory size for trajectory classification. However, although it outperformed the state-of-the-art in terms of accuracy, MASTERMovelets is computationally expensive and results in a high dimensionality problem, which makes it unfeasible for most real trajectory datasets that contain a big volume of data. To overcome this problem and enable the application of the movelets approach on large datasets, in this paper we propose a new high-performance method for extracting movelets and classifying trajectories, called HiPerMovelets (High-performance Movelets). Experimental results show that HiPerMovelets is 10 times faster than MASTERMovelets, reduces the high-dimensionality problem, is more scalable, and presents a high classification accuracy in all evaluated datasets with both raw and semantic trajectories. Numéro de notice : A2022-332 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2021.2018593 Date de publication en ligne : 03/01/2022 En ligne : https://doi.org/10.1080/13658816.2021.2018593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100608
in International journal of geographical information science IJGIS > vol 36 n° 5 (May 2022) . - pp 1012 - 1036[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2022051 SL Revue Centre de documentation Revues en salle Disponible Human cognition based framework for detecting roads from remote sensing images / Naveen Chandra in Geocarto international, vol 37 n° 8 ([01/05/2022])
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Titre : Human cognition based framework for detecting roads from remote sensing images Type de document : Article/Communication Auteurs : Naveen Chandra, Auteur ; Himadri Vaidya, Auteur ; Jayanta Kumar Ghosh, Auteur Année de publication : 2022 Article en page(s) : pp 2365 - 2384 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse d'image numérique
[Termes IGN] classification
[Termes IGN] cognition
[Termes IGN] extraction du réseau routier
[Termes IGN] image à haute résolution
[Termes IGN] interprétation (psychologie)
[Termes IGN] représentation cognitive
[Termes IGN] segmentation d'imageRésumé : (auteur) The complete extraction of roads from remote sensing images (RSIs) is an emergent area of research. It is an interesting topic as it involves diverse procedures for detecting roads. The detection of roads using high-resolution-satellite-images (HRSi) is challenging because of the occurrence of several types of noise such as bridges, vehicles, and crossing lines, etc. The extraction of the correct road network is crucial due to its broad range of applications such as transportation, map updating, navigation, and generating maps. Therefore our paper concentrates on understanding the cognitive processes, reasoning, and knowledge used by the analyst through visual cognition while performing the task of road detection from HRSi. The novel process is performed emulating human cognition within cognitive task analysis which is carried out in five different stages. The suggested cognitive procedure for road extraction is validated with the fifteen HRSi of four different land cover patterns specifically developed-sub-urban (DSUr), developed-urban (DUr), emerging-sub-urban (ESUr), and emerging-urban (EUr). The experimental results and the comparative assessment prove the impact of the presented cognitive method. Numéro de notice : A2022-506 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1810330 Date de publication en ligne : 14/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1810330 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101027
in Geocarto international > vol 37 n° 8 [01/05/2022] . - pp 2365 - 2384[article]Impacts of spatiotemporal resolution and tiling on SLEUTH model calibration and forecasting for urban areas with unregulated growth patterns / Damilola Eyelade in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)
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Titre : Impacts of spatiotemporal resolution and tiling on SLEUTH model calibration and forecasting for urban areas with unregulated growth patterns Type de document : Article/Communication Auteurs : Damilola Eyelade, Auteur ; Keith C. Clarke, Auteur ; Ighodalo Ijagbone, Auteur Année de publication : 2022 Article en page(s) : pp 1037 - 1058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] changement d'utilisation du sol
[Termes IGN] croissance urbaine
[Termes IGN] dalle
[Termes IGN] données spatiotemporelles
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle de simulation
[Termes IGN] modélisation spatiale
[Termes IGN] Nigéria
[Termes IGN] OpenStreetMapRésumé : (auteur) The SLEUTH model provides a framework for understanding land use evolution around urban areas. Calibration of SLEUTH’s behavioral coefficients can be impacted by scale and nonlinear transitions due to the SLEUTH land use deltatron module’s assumption of linear Markov change probabilities. This study attempted to establish what spatial resolution and temporal scale produces the most accurate forecasts given the linear change assumption. The impact of tiling the input data was also examined. To determine these, SLEUTH was calibrated at four spatial and three temporal scales for Ibadan, Nigeria using both untiled and tiled data. Calibration results were evaluated using accuracy metrics including Figure of Merit (FOM) and mean uncertainty. The best mix of calibration metrics (FOM 0.26) and mean uncertainty (11.64) was achieved at 30 m resolution and an intermediate temporal interval. Tiling input data led to overfitting, allowing good model fit within individual tiles but a reduction in trend recognition across land use types. Subsequently, a 2040 projection that is as accurate as possible, and scientifically justifiable given the available data, was produced. The findings provide a framework for understanding the effect of spatiotemporal scale on SLEUTH inputs that require tiling particularly for urban areas in the global south. Numéro de notice : A2022-347 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2011292 Date de publication en ligne : 16/12/2021 En ligne : https://doi.org/10.1080/13658816.2021.2011292 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100531
in International journal of geographical information science IJGIS > vol 36 n° 5 (May 2022) . - pp 1037 - 1058[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2022051 SL Revue Centre de documentation Revues en salle Disponible Landslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China / Kezhen Yao in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)
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Titre : Landslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China Type de document : Article/Communication Auteurs : Kezhen Yao, Auteur ; Saini Yang, Auteur ; Shengnan Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] cartographie des risques
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] dispersion
[Termes IGN] effondrement de terrain
[Termes IGN] Extreme Gradient Machine
[Termes IGN] modèle de simulation
[Termes IGN] régression linéaire
[Termes IGN] risque naturel
[Termes IGN] vulnérabilitéRésumé : (auteur) Landslide susceptibility assessment serves as a critical scientific reference for geohazard control, land use, and sustainable development planning. The existing research has not fully considered the potential impact of the spatial agglomeration and dispersion of landslides on assessments. This issue may cause a systematic evaluation bias when the field investigation data are insufficient, which is common due to limited human resources. Accordingly, this paper proposes two novel strategies, including a clustering algorithm and a preprocessing method, for these two ignored features to strengthen assessments, especially in high-susceptibility regions. Multiple machine learning models are compared in a case study of the city of Bijie (Guizhou Province, China). Then we generate the optimal susceptibility map and conduct two experiments to test the validity of the proposed methods. The primary conclusions of this study are as follows: (1) random forest (RF) was superior to other algorithms in the recognition of high-susceptibility areas and the portrayal of local spatial features; (2) the susceptibility map incorporating spatial feature messages showed a noticeable improvement over the spatial distribution and gradual change of susceptibility, as well as the accurate delineation of critical hazardous areas and the interpretation of historical hazards; and (3) the spatial distribution feature had a significant positive effect on modeling, as the accuracy increased by 5% and 10% after including the spatial agglomeration and dispersion consideration in the RF model, respectively. The benefit of the agglomeration is concentrated in high-susceptibility areas, and our work provides insight to improve the assessment accuracy in these areas, which is critical to risk assessment and prevention activities. Numéro de notice : A2022-371 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11050269 Date de publication en ligne : 19/04/2022 En ligne : https://doi.org/10.3390/ijgi11050269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100613
in ISPRS International journal of geo-information > vol 11 n° 5 (May 2022) . - n° 269[article]Mapping and prediction of soil organic carbon by an advanced geostatistical technique using remote sensing and terrain data / Santanu Malik in Geocarto international, vol 37 n° 8 ([01/05/2022])
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Titre : Mapping and prediction of soil organic carbon by an advanced geostatistical technique using remote sensing and terrain data Type de document : Article/Communication Auteurs : Santanu Malik, Auteur ; Tridip Bhowmik, Auteur ; Umesh Mishra, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2198 - 2214 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte d'occupation du sol
[Termes IGN] estimation bayesienne
[Termes IGN] géostatistique
[Termes IGN] gestion durable
[Termes IGN] Inde
[Termes IGN] krigeage
[Termes IGN] modèle de simulation
[Termes IGN] puits de carbone
[Termes IGN] régression
[Termes IGN] réseau neuronal artificiel
[Termes IGN] sol arableRésumé : (auteur) Prediction and accurate digital soil mapping (DSM) of soil organic carbon (SOC) at a local scale is a key factor for any agro-ecological modelling. This study aims to use remote sensing and terrain derivatives to provide a reliable method for SOC prediction. An advanced geostatistical-based empirical Bayesian Kriging regression (EBKR) method was used and performance was compared with the artificial neural network (ANN) and hybrid ANN, i.e. ANN-OK (ordinary kriging) and ANN-CK (cokriging). The result showed that the hybrid ANN model performs better than ANN, whereas the EBKR method outperforms all other methods with the highest R2 of 0.936. The DSM map shows that the highest SOC concentration was found in easternmost part of the study area with grass and agricultural land. This work shows the robustness of the EBKR prediction method over other techniques. The study will also aid the policymakers in adopting sustainable land use management. Numéro de notice : A2022-505 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1815864 Date de publication en ligne : 24/09/2020 En ligne : https://doi.org/10.1080/10106049.2020.1815864 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101026
in Geocarto international > vol 37 n° 8 [01/05/2022] . - pp 2198 - 2214[article]Multi-modal temporal attention models for crop mapping from satellite time series / Vivien Sainte Fare Garnot in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)
PermalinkA novel ionospheric mapping function modeling at regional scale using empirical orthogonal functions and GNSS data / Peng Chen in Journal of geodesy, vol 96 n° 5 (May 2022)
PermalinkPlastic waste cleanup priorities to reduce marine pollution: A spatiotemporal analysis for Accra and Lagos with satellite data / Susmita Dasgupta in Science of the total environment, vol 839 (May 2022)
PermalinkRevising cadastral data on land boundaries using deep learning in image-based mapping / Bujar Fetai in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)
PermalinkSmartphone digital photography for fractional vegetation cover estimation / Gaofei Yin in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)
PermalinkThe role of blue green infrastructure in the urban thermal environment across seasons and local climate zones in East Africa / Xueqin Li in Sustainable Cities and Society, vol 80 (May 2022)
PermalinkUnsupervised multi-view CNN for salient view selection and 3D interest point detection / Ran Song in International journal of computer vision, vol 130 n° 5 (May 2022)
PermalinkWeakly supervised semantic segmentation of airborne laser scanning point clouds / Yaping Lin in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)
PermalinkCrop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information / Murali Krishna Gumma in Geocarto international, vol 37 n° 7 ([15/04/2022])
PermalinkSpectral-spatial classification method for hyperspectral images using stacked sparse autoencoder suitable in limited labelled samples situation / Seyyed Ali Ahmadi in Geocarto international, vol 37 n° 7 ([15/04/2022])
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