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scientifique

Geographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in Cartography and Geographic Information Science, vol 49 n° inconnu (2022)  

Public [article]  inCartography and Geographic Information Science > vol 49 n° inconnu (2022) Titre : Geographically masking addresses to study COVID-19 clusters Type de document : Article/Communication Auteurs : Walid Houfaf-Khoufaf, Auteur ; Guillaume Touya , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] adresse postale [Termes IGN] anonymisation [Termes IGN] carte sanitaire [Termes IGN] classification barycentrique [Termes IGN] surveillance sanitaire [Termes IGN] traitement de données localisées Résumé : (auteur) The spatio-temporal analysis of cases is a good way an epidemic, and the recent COVID-19 pandemic unfortunately generated a huge amount of data. But analysing this raw data, with for instance the address of the people who contracted COVID-19, raises some privacy issues, and geomasking is necessary to preserve both people privacy and the spatial accuracy required for analysis. This paper proposes dierent geomasking techniques adapted to this COVID-19 data. Methods: Different techniques are adapted from the literature, and tested on a synthetic dataset mimicking the COVID-19 spatio-temporal spreading in Paris and a more rural nearby region. Theses techniques are assessed in terms of k-anonymity and cluster preservation. Results: Three adapted geomasking techniques are proposed: aggregation, bimodal gaussian perturbation, and simulated crowding. All three can be useful in different use cases, but the bimodal gaussian perturbation is the overall best techniques, and the simulated crowding is the most promising one, provided some improvements are introduced to avoid points with a low k-anonymity. Conclusions: It is possible to use geomasking techniques on addresses of people who caught COVID-19, while preserving the important spatial patterns. Numéro de notice : A2021-065 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Autre URL associée : vers RSquare Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.1977709 Date de publication en ligne : 08/10/2021 En ligne : https://doi.org/10.1080/15230406.2021.1977709 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96857 [article]

A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery / Lan Xun in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 181 (November 2021) . - pp 148 - 166 Titre : A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery Type de document : Article/Communication Auteurs : Lan Xun, Auteur ; Jiahua Zhang, Auteur ; Dan Cao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 148 - 166 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] cartographie automatique [Termes IGN] Chine [Termes IGN] coefficient de rétrodiffusion [Termes IGN] distribution spatiale [Termes IGN] Etats-Unis [Termes IGN] Gossypium (genre) [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] indice de végétation [Termes IGN] polarisation [Termes IGN] réflectance spectrale [Termes IGN] série temporelle Résumé : (auteur) Cotton is an important cash crop in the world, as the main source of natural and renewable fiber for textiles. Accurate and timely monitoring of the cotton distribution is crucial for cotton cultivation management and international trade. However, most of the previous researches on cotton identification using remotely sensed images are highly dependent on training samples, and the collection of samples is time-consuming and expensive. To overcome this limitation, a new index, termed as Cotton Mapping Index (CMI), was developed in this study for automatic cotton mapping using time series of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) satellite data. Four sites in the United States (U.S.) and four sites in China were selected to develop and assess the performance of the CMI. The spectral characteristics derived from Sentinel-2 and backscattering coefficients derived from Sentinel-1 for cotton and non-cotton crops during the cotton growth period were analyzed. Considering the phenology differences of crops in different regions, the features at an adaptive window were adopted to construct the CMI. The results showed that at the peak greenness period, the multiplication of red-edge 1 and red-edge 2 band for cotton samples were much larger than those for non-cotton samples, whereas the spectral angle at the red band as well as the absolute values of backscattering coefficients in vertical transmit and vertical receive (VV) polarization for cotton samples were much smaller than those for non-cotton samples. Based on these findings, the CMI was developed to identify cotton cultivated area within the cropland area. The overall accuracy of classification results for the sites in the U.S. was higher than 81.20%, and the mean relative error for the sites in Xinjiang of China was 26.69%. The CMI, which incorporated optical and radar features, had a better performance than the indices using optical features solely. The advantage of the CMI over supervised classifiers (i.e., k-nearest neighbors, support vector machine and random forest) is that no training samples are required. Moreover, the cotton distribution map can be obtained before the harvest using the CMI. These results indicated the potential of the CMI for cotton mapping. The applicability of CMI in other regions with different cropping systems and crop types needs to be further assessed in the future study. Numéro de notice : A2021-775 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.08.021 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.08.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98836 [article]

Pose estimation and 3D reconstruction of vehicles from stereo-images using a subcategory-aware shape prior / Maximilian Alexander Coenen in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 181 (November 2021) . - pp 27 - 47 Titre : Pose estimation and 3D reconstruction of vehicles from stereo-images using a subcategory-aware shape prior Type de document : Article/Communication Auteurs : Maximilian Alexander Coenen, Auteur ; Franz Rottensteiner, Auteur Année de publication : 2021 Article en page(s) : pp 27 - 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] estimation de pose [Termes IGN] modèle stochastique [Termes IGN] problème inverse [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] robotique [Termes IGN] véhicule automobile [Termes IGN] vision par ordinateur Résumé : (auteur) The 3D reconstruction of objects is a prerequisite for many highly relevant applications of computer vision such as mobile robotics or autonomous driving. To deal with the inverse problem of reconstructing 3D objects from their 2D projections, a common strategy is to incorporate prior object knowledge into the reconstruction approach by establishing a 3D model and aligning it to the 2D image plane. However, current approaches are limited due to inadequate shape priors and the insufficiency of the derived image observations for a reliable alignment with the 3D model. The goal of this paper is to show how 3D object reconstruction can profit from a more sophisticated shape prior and from a combined incorporation of different observation types inferred from the images. We introduce a subcategory-aware deformable vehicle model that makes use of a prediction of the vehicle type for a more appropriate regularisation of the vehicle shape. A multi-branch CNN is presented to derive predictions of the vehicle type and orientation. This information is also introduced as prior information for model fitting. Furthermore, the CNN extracts vehicle keypoints and wireframes, which are well-suited for model-to-image association and model fitting. The task of pose estimation and reconstruction is addressed by a versatile probabilistic model. Extensive experiments are conducted using two challenging real-world data sets on both of which the benefit of the developed shape prior can be shown. A comparison to state-of-the-art methods for vehicle pose estimation shows that the proposed approach performs on par or better, confirming the suitability of the developed shape prior and probabilistic model for vehicle reconstruction. Numéro de notice : A2021-772 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.006 Date de publication en ligne : 14/09/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98829 [article]

Estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models / Arne Nothdurft in Forest ecology and management, vol 502 (15 december 2021)  

Public [article]  inForest ecology and management > vol 502 (15 december 2021) . - n° 119714 Titre : Estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models Type de document : Article/Communication Auteurs : Arne Nothdurft, Auteur ; Christoph Gollob, Auteur ; Ralf Krasnitzer, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119714 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Autriche [Termes IGN] bois sur pied [Termes IGN] dommage forestier causé par facteurs naturels [Termes IGN] échantillonnage [Termes IGN] estimation bayesienne [Termes IGN] inventaire forestier étranger (données) [Termes IGN] lasergrammétrie [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] modèle de régression [Termes IGN] modèle mathématique [Termes IGN] tempête [Termes IGN] volume en bois [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) A spatial regression model framework is presented to predict growing stock volume loss due to storm Adrian which caused heavy forest damage in the upper Gail valley in Carinthia, Austria, in October 2018. Model parameters were estimated using growing stock volume measured with a terrestrial laser scanner on 62 sample plots distributed across five sub-regions. Predictor variables were derived from high resolution vegetation height measurements collected during an airborne laser scanning campaign. Non-spatial and spatial candidate models were proposed and assessed based on fit to observed data and out-of-sample prediction. Spatial Gaussian processes associated model intercepts and regression coefficients were used to capture spatial dependence. Results show a spatially-varying coefficient model, which allowed the intercept and regression coefficients to vary spatially, yielded the best fit and prediction. Two approaches were considered for prediction over blowdown areas: 1) an areal approach that viewed each blowdown as a single prediction unit indexed by its centroid; and 2) a block approach where each blowdown was partitioned into smaller prediction units to better align with sample plots’ spatial support. Joint prediction was used to acknowledge spatial dependence among block units. Results demonstrated the block approach is preferable as it mitigated change-of-support issues encountered in the areal approach. Despite the small sample size, predictions for 55% of the total 564 blowdown areas, accounting for 93% of the total loss, had a coefficient of variation less than 25%. Key advantages of the proposed regression framework and chosen Bayesian inferential paradigm, were the ability to quantify uncertainty in spatial covariance parameters, propagate parameter uncertainty through to prediction, and provide statistically valid prediction point and interval estimates for individual blowdowns and collections of blowdowns at the sub-region and region scale via posterior predictive distribution summaries. Numéro de notice : A2021-770 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2021.119714 Date de publication en ligne : 07/10/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119714 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98822 [article]

A method for precisely predicting satellite clock bias based on robust fitting of ARMA models / Guochao Zhang in GPS solutions, vol 26 n° 1 (January 2022)  

Public [article]  inGPS solutions > vol 26 n° 1 (January 2022) . - n° 3 Titre : A method for precisely predicting satellite clock bias based on robust fitting of ARMA models Type de document : Article/Communication Auteurs : Guochao Zhang, Auteur ; Songhui Han, Auteur ; Jun Ye, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] décalage d'horloge [Termes IGN] erreur systématique interfréquence d'horloge [Termes IGN] estimation bayesienne [Termes IGN] international GPS service for geodynamics [Termes IGN] série temporelle [Termes IGN] statistique mathématique [Termes IGN] valeur aberrante Résumé : (auteur) The precise satellite clock bias prediction is critical in improving the positioning, navigation and timing (PNT) service capabilities of the global navigation satellite system (GNSS). Due to the influence of satellite signal path and the observation environment, the satellite clock bias data usually contain outliers that heavily affect the accuracy of satellite clock bias prediction. Based on the time series ARMA model and Bayes statistical theory, we propose a method to precisely predict satellite clock bias and detect outliers in the historical sequence of satellite clock bias. At first, considering the effects of an additive outlier (AO) and innovative outlier (IO), a labeling model for robustly fitting the time series ARMA model and detecting AOs and IOs simultaneously is constructed based on the labeling method of classification variables. Second, the Bayes method for robustly fitting time series ARMA model is proposed based on the Bayes statistical theory. Furthermore, it develops an algorithm to precisely predict satellite clock bias using the Bayes method for robustly fitting the time series ARMA model mentioned above. Finally, in order to illustrate the performance of the method for precisely predicting satellite clock bias that we presented, three examples are designed based on the real GPS data come from the IGS official website, and the prediction results of the method are compared with that of original ARMA model (oARMA), quadratic polynomial model (QP) and gray model (GM). It is found that the method can precisely predict the satellite clock bias as well as accurately detect the outliers in the historical sequence. Numéro de notice : A2022-002 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01182-3 Date de publication en ligne : 20/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01182-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98827 [article]

A mean-squared-error condition for weighting ionospheric delays in GNSS baselines / Peter J.G. Teunissen in Journal of geodesy, vol 95 n° 11 (November 2021)  

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Potential flood hazard zone mapping based on geomorphologic considerations and fuzzy analytical hierarchy model in a data scarce West African basin / Olabanji Aladejana in Geocarto international, Vol 36 n° 19 ([18/10/2021])  

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A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area / Myung-Jin Jun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

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Multiscale geographically and temporally weighted regression with a unilateral temporal weighting scheme and its application in the analysis of spatiotemporal characteristics of house prices in Beijing / Zhi Zhang in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

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A quantitative comparison of regionalization methods / Orhun Aydun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

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A spatial model of cognitive distance in cities / Ed Manley in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

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Spatially–encouraged spectral clustering: a technique for blending map typologies and regionalization / Levi John Wolf in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

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A topic model based framework for identifying the distribution of demand for relief supplies using social media data / Ting Zhang in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

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Urban land-use analysis using proximate sensing imagery: a survey / Zhinan Qiao in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

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A vector-based method for drainage network analysis based on LiDAR data / Fangzheng Lyu in Computers & geosciences, vol 156 (November 2021)  

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