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3D map creation using crowdsourced GNSS data / Terence Lines in Computers, Environment and Urban Systems, vol 89 (September 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 89 (September 2021) Titre : 3D map creation using crowdsourced GNSS data Type de document : Article/Communication Auteurs : Terence Lines, Auteur ; Anahid Basiri, Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] approche participative [Termes IGN] Bootstrap (statistique) [Termes IGN] cartographie 3D [Termes IGN] données GNSS [Termes IGN] données localisées 2,5D [Termes IGN] hauteur du bâti [Termes IGN] interface de programmation [Termes IGN] régression logistique [Termes IGN] signal GNSS [Termes IGN] trajet multiple [Termes IGN] vision par ordinateur Résumé : (auteur) 3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. The paper applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard. Numéro de notice : A2021-535 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101671 date de publication en ligne : 19/06/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101671 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97998 [article]

Vectorial integer bootstrapping: flexible integer estimation with application to GNSS / Peter J.G. Teunissen in Journal of geodesy, vol 95 n° 9 (September 2021)  

Public [article]  inJournal of geodesy > vol 95 n° 9 (September 2021) . - n° 99 Titre : Vectorial integer bootstrapping: flexible integer estimation with application to GNSS Type de document : Article/Communication Auteurs : Peter J.G. Teunissen, Auteur ; L. Massarweh, Auteur ; S. Verhagen, Auteur Année de publication : 2021 Article en page(s) : n° 99 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] ambiguïté entière [Termes IGN] Bootstrap (statistique) [Termes IGN] estimateur [Termes IGN] mesurage de phase [Termes IGN] phase GNSS [Termes IGN] positionnement par GNSS [Termes IGN] résolution d'ambiguïté [Termes IGN] transformation en Z Résumé : (auteur) In this contribution, we extend the principle of integer bootstrapping (IB) to a vectorial form (VIB). The mathematical definition of the class of VIB-estimators is introduced together with their pull-in regions and other properties such as probability bounds and success rate approximations. The vectorial formulation allows sequential block-by-block processing of the ambiguities based on a user-chosen partitioning. In this way, flexibility is created, where for specific choices of partitioning, tailored VIB-estimators can be designed. This wide range of possibilities is discussed, supported by numerical simulations and analytical examples. Further guidelines are provided, as well as the possible extension to other classes of estimators. Numéro de notice : A2021-620 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01552-2 date de publication en ligne : 16/08/2021 En ligne : https://doi.org/10.1007/s00190-021-01552-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98236 [article]

Ensemble learning for hyperspectral image classification using tangent collaborative representation / Hongjun Su in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 3778 - 3790 Titre : Ensemble learning for hyperspectral image classification using tangent collaborative representation Type de document : Article/Communication Auteurs : Hongjun Su, Auteur ; Yao Yu, Auteur ; Qian Du, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3778 - 3790 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image numérique [Termes IGN] boosting adapté [Termes IGN] Bootstrap (statistique) [Termes IGN] classification [Termes IGN] classification dirigée [Termes IGN] classification orientée objet [Termes IGN] conception collaborative [Termes IGN] données d'apprentissage [Termes IGN] échantillon [Termes IGN] image hyperspectrale [Termes IGN] neurone artificiel [Termes IGN] performance [Termes IGN] régression Résumé : (auteur) Recently, collaborative representation classification (CRC) has attracted much attention for hyperspectral image analysis. In particular, tangent space CRC (TCRC) has achieved excellent performance for hyperspectral image classification in a simplified tangent space. In this article, novel Bagging-based TCRC (TCRC-bagging) and Boosting-based TCRC (TCRC-boosting) methods are proposed. The main idea of TCRC-bagging is to generate diverse TCRC classification results using the bootstrap sample method, which can enhance the accuracy and diversity of a single classifier simultaneously. For TCRC-boosting, it can provide the most informative training samples by changing their distributions dynamically for each base TCRC learner. The effectiveness of the proposed methods is validated using three real hyperspectral data sets. The experimental results show that both TCRC-bagging and TCRC-boosting outperform their single classifier counterpart. In particular, the TCRC-boosting provides superior performance compared with the TCRC-bagging. Numéro de notice : A2020-280 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2957135 date de publication en ligne : 01/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2957135 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95100 [article]

Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM) / Chanwoo Jin in Transactions in GIS, Vol 24 n° 1 (February 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 1 (February 2020) . - pp 104 - 122 Titre : Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM) Type de document : Article/Communication Auteurs : Chanwoo Jin, Auteur ; Atsushi Nara, Auteur ; Jiue-An Yang, Auteur ; Ming-Hsiang Tsou, Auteur Année de publication : 2020 Article en page(s) : pp 104 - 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Bootstrap (statistique) [Termes IGN] Californie (Etats-Unis) [Termes IGN] carte de mobilité [Termes IGN] données issues des réseaux sociaux [Termes IGN] interpolation par pondération de zones [Termes IGN] mesure de similitude [Termes IGN] mobilité [Termes IGN] mobilité urbaine [Termes IGN] origine - destination Résumé : (auteur) Understanding diverse characteristics of human mobility provides profound knowledge of urban dynamics and complexity. Human movements are recorded in a variety of data sources and each describes unique mobility characteristics. Revealing similarity and difference in mobility data sources facilitates grasping comprehensive human mobility patterns. This study introduces a new method to measure similarities on two origin–destination (OD) matrices by spatially extending an image‐assessment tool, the structural similarity index (SSIM). The new measurement, spatially weighted SSIM (SpSSIM), utilizes weight matrices to overcome the SSIM sensitivity issue due to the ordering of OD pairs by explicitly defining spatial adjacency. To evaluate SpSSIM, we compared performances between SSIM and SpSSIM with resampling the orders of OD pairs and conducted bootstrapping to test the statistical significance of SpSSIM. As a case study, we compared OD matrices generated from three data sources in San Diego County, CA: U.S. Census‐based Longitudinal Employer–Household Dynamics Origin–Destination employment statistics, Twitter, and Instagram. The case study demonstrated that SpSSIM was able to capture similarities of mobility patterns between datasets that varied by distance. Some regions showed local dissimilarity while the global index indicated they were similar. The results enhance the understanding of complex mobility patterns from various datasets, including social media. Numéro de notice : A2020-104 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12590 date de publication en ligne : 23/10/2019 En ligne : https://doi.org/10.1111/tgis.12590 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94698 [article]

Parametric bootstrap estimators for hybrid inference in forest inventories / Mathieu Fortin in Forestry, an international journal of forest research, vol 91 n° 3 (July 2018)  

Public [article]  inForestry, an international journal of forest research > vol 91 n° 3 (July 2018) . - pp 354 - 365 Titre : Parametric bootstrap estimators for hybrid inference in forest inventories Type de document : Article/Communication Auteurs : Mathieu Fortin, Auteur ; Ruben Manso, Auteur ; Robert Schneider, Auteur Année de publication : 2018 Article en page(s) : pp 354 - 365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Bootstrap (statistique) [Termes IGN] complexité [Termes IGN] erreur systématique [Termes IGN] inférence statistique [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle stochastique [Termes IGN] Québec (Canada) [Termes IGN] variance [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) In forestry, the variable of interest is not always directly available from forest inventories. Consequently, practitioners have to rely on models to obtain predictions of this variable of interest. This context leads to hybrid inference, which is based on both the probability design and the model. Unfortunately, the current analytical hybrid estimators for the variance of the point estimator are mainly based on linear or nonlinear models and their use is limited when the model reaches a high level of complexity. An alternative consists of using a variance estimator based on resampling methods (Rubin, D. B. (1987). Multiple imputation for nonresponse surveys. John Wiley & Sons, Hoboken, New Jersey, USA). However, it turns out that a parametric bootstrap (BS) estimator of the variance can be biased in contexts of hybrid inference. In this study, we designed and tested a corrected BS estimator for the variance of the point estimator, which can easily be implemented as long as all of the stochastic components of the model can be properly simulated. Like previous estimators, this corrected variance estimator also makes it possible to distinguish the contribution of the sampling and the model to the variance of the point estimator. The results of three simulation studies of increasing complexity showed no evidence of bias for this corrected variance estimator, which clearly outperformed the BS variance estimator used in previous studies. Since the implementation of this corrected variance estimator is not much more complicated, we recommend its use in contexts of hybrid inference based on complex models. Numéro de notice : A2018-637 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpx048 date de publication en ligne : 22/11/2017 En ligne : https://doi.org/10.1093/forestry/cpx048 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93246 [article]

Comparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs / Abraham Montoya Obeso (2018)  

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Regression Modeling Strategies / Frank E. Harrell Jr. (2015)  

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Properties of boundary-line release criteria in North American tree species / Bryan A. Black in Annals of Forest Science, Vol 66 n° 2 (march 2009)  

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