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Potential of Bayesian formalism for the fusion and assimilation of sequential forestry data in time and space / Cheikh Mohamedou in Canadian Journal of Forest Research, Vol 52 n° 4 (April 2022)
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Titre : Potential of Bayesian formalism for the fusion and assimilation of sequential forestry data in time and space Type de document : Article/Communication Auteurs : Cheikh Mohamedou, Auteur ; Annika S. Kangas, Auteur ; Alireza Hamedianfar, Auteur ; Jari Vauhkonen, Auteur Année de publication : 2022 Article en page(s) : pp 439 - 449 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données spatiotemporelles
[Termes IGN] dynamique de la végétation
[Termes IGN] estimation bayesienne
[Termes IGN] fusion de données
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] série temporelle
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Forest resource assessments based on multi-source and multi-temporal data have become more common. Therefore, enhancing the prediction capabilities of forestry dynamics by efficiently pooling and analyzing time-series and spatial sequential data is now more pivotal. Bayesian filtering and smoothing provide a well-defined formalism for the fusion or assimilation of various data. We ascertained how often the generic, standardized Bayesian framework is used in the scientific literature and whether such an approach is beneficial for forestry applications. A review of the literature showed that the use of Bayesian methods appears to be less common in forestry than in other disciplines, particularly remote sensing. Specifically, time-series analyses were found to favor ad hoc methods. Our review did not reveal strong numeric evidence for better performance by the various Bayesian approaches, but this result may be partly due to the challenge in comparing a variety of methods for different prediction tasks. We identified methodological challenges related to assimilating predictions of forest development; in particular, combining modelled growth with disturbances due to both forest operations and natural phenomena. Nevertheless, the Bayesian frameworks provide possibilities to efficiently combine and update prior and posterior predictive distributions and derive related uncertainty measures that appear under-utilized in forestry. Numéro de notice : A2022-315 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1139/cjfr-2021-0145 Date de publication en ligne : 17/01/2022 En ligne : https://doi.org/10.1139/cjfr-2021-0145 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100415
in Canadian Journal of Forest Research > Vol 52 n° 4 (April 2022) . - pp 439 - 449[article]Uncertainty estimation for stereo matching based on evidential deep learning / Chen Wang in Pattern recognition, vol 124 (April 2022)
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Titre : Uncertainty estimation for stereo matching based on evidential deep learning Type de document : Article/Communication Auteurs : Chen Wang, Auteur ; Xiang Wang, Auteur ; Jiawei Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108498 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] apprentissage profond
[Termes IGN] distribution de Gauss
[Termes IGN] fonction de perte
[Termes IGN] lissage de données
[Termes IGN] modèle d'incertitude
[Termes IGN] reconstruction d'imageRésumé : (auteur) Although deep learning-based stereo matching approaches have achieved excellent performance in recent years, it is still a non-trivial task to estimate the uncertainty of the produced disparity map. In this paper, we propose a novel approach to estimate both aleatoric and epistemic uncertainties for stereo matching in an end-to-end way. We introduce an evidential distribution, named Normal Inverse-Gamma (NIG) distribution, whose parameters can be used to calculate the uncertainty. Instead of directly regressed from aggregated features, the uncertainty parameters are predicted for each potential disparity and then averaged via the guidance of matching probability distribution. Furthermore, considering the sparsity of ground truth in real scene datasets, we design two additional losses. The first one tries to enlarge uncertainty on incorrect predictions, so uncertainty becomes more sensitive to erroneous regions. The second one enforces the smoothness of the uncertainty in the regions with smooth disparity. Most stereo matching models, such as PSM-Net, GA-Net, and AA-Net, can be easily integrated with our approach. Experiments on multiple benchmark datasets show that our method improves stereo matching results. We prove that both aleatoric and epistemic uncertainties are well-calibrated with incorrect predictions. Particularly, our method can capture increased epistemic uncertainty on out-of-distribution data, making it effective to prevent a system from potential fatal consequences. Code is available at https://github.com/Dawnstar8411/StereoMatching-Uncertainty. Numéro de notice : A2022-198 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.patcog.2021.108498 Date de publication en ligne : 23/12/2021 En ligne : https://doi.org/10.1016/j.patcog.2021.108498 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99992
in Pattern recognition > vol 124 (April 2022) . - n° 108498[article]Changing mobility patterns in the Netherlands during COVID-19 outbreak / Sander Van Der Drift in Journal of location-based services, vol 16 n° 1 (March 2022)
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Titre : Changing mobility patterns in the Netherlands during COVID-19 outbreak Type de document : Article/Communication Auteurs : Sander Van Der Drift, Auteur ; Luc Wismans, Auteur ; Marie-José Olde-Kalter, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 24 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] bicyclette
[Termes IGN] comportement
[Termes IGN] épidémie
[Termes IGN] estimation bayesienne
[Termes IGN] mobilité territoriale
[Termes IGN] Pays-Bas
[Termes IGN] téléphone intelligent
[Termes IGN] transport
[Termes IGN] transport public
[Termes IGN] travail à domicile
[Termes IGN] véhicule automobileRésumé : (auteur) The COVID-19 outbreak and associated measures taken had an enormous impact on society as well as a disruptive, but not necessarily negative, impact on mobility. The Ministry of Infrastructure and Water Management received the most recent insights from the Dutch Mobility Panel (DMP) on a weekly basis. These insights were used to monitor the travel behaviour and to analyse changes in the behaviour of different groups and usage of modes of transport during COVID-19. The analysis shows an enormous decrease in travel at the beginning of the implementation of the so-called ‘intelligent’ lockdown and gradual increase again towards comparable levels as before this ‘intelligent lockdown, although the distribution over time, motives and used modes has changed. It becomes clear that not everyone needs to travel during peak hours and commuter travel is also not the main reason for the increase in car usage. Furthermore, cycling has shown to be an alternative option for travellers and public transport is hardly used anymore. If it is possible to sustain the lower level of car usage and integrate public transport as an important alternative for travel again, the COVID-19 impact on mobility could have a substantial remaining positive impact on mobility. Numéro de notice : A2022-391 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17489725.2021.1876259 Date de publication en ligne : 11/03/2021 En ligne : https://doi.org/10.1080/17489725.2021.1876259 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100682
in Journal of location-based services > vol 16 n° 1 (March 2022) . - pp 1 - 24[article]Deep-learning-based multispectral image reconstruction from single natural color RGB image - Enhancing UAV-based phenotyping / Jiangsan Zhao in Remote sensing, vol 14 n° 5 (March-1 2022)
[article]
Titre : Deep-learning-based multispectral image reconstruction from single natural color RGB image - Enhancing UAV-based phenotyping Type de document : Article/Communication Auteurs : Jiangsan Zhao, Auteur ; Ajay Kumar, Auteur ; Balaji Naik Banoth, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1272; Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agriculture de précision
[Termes IGN] apprentissage profond
[Termes IGN] erreur absolue
[Termes IGN] image multibande
[Termes IGN] image RVB
[Termes IGN] Inde
[Termes IGN] phénologie
[Termes IGN] reconstruction d'imageRésumé : (auteur) Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus aim to generate high spatial MSIs through a robust, deep-learning-based reconstruction method using ncRGB images. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAEloss) and spectral information divergence (SIDloss) were most effective during the building of both models, while models using the MRAEloss function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using “reconstructed” NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture. Numéro de notice : A2022-210 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14051272 Date de publication en ligne : 05/03/2022 En ligne : https://doi.org/10.3390/rs14051272 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100033
in Remote sensing > vol 14 n° 5 (March-1 2022) . - n° 1272;[article]ReBankment: displacing embankment lines from roads and rivers with a least squares adjustment / Guillaume Touya in International journal of cartography, vol 8 n° 1 (March 2022)
[article]
Titre : ReBankment: displacing embankment lines from roads and rivers with a least squares adjustment Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Imran Lokhat , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 37 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de généralisation
[Termes IGN] compensation par moindres carrés
[Termes IGN] données topographiques
[Termes IGN] talus
[Vedettes matières IGN] GénéralisationRésumé : (auteur) While the recent progress on automated generalisation helped National Mapping Agencies to derive topographic maps more and more quickly, there are still practical cartographic issues that require attention. For instance, embankments are represented with line symbols showing the slope of the embankment. This paper proposes an automated algorithm called ReBankment that displaces the embankment lines from the roads and rivers that overlap the embankment symbol. ReBankment is based on a triangulation to identify neighbourhoods, and on a least squares adjustment to displace and distort the embankment line while preserving its shape. This paper also proposes how to handle complex cases and scaling issues. ReBankment is tested on real data from a 1:25k scale topographic map. Numéro de notice : A2022-006 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2021.1972787 Date de publication en ligne : 18/10/2021 En ligne : https://doi.org/10.1080/23729333.2021.1972787 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98838
in International journal of cartography > vol 8 n° 1 (March 2022) . - pp 37 - 53[article]Understanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea / Yang Xu in Computers, Environment and Urban Systems, vol 92 (March 2022)PermalinkCompetition and climate influence in the basal area increment models for Mediterranean mixed forests / Diego Rodríguez de Prado in Forest ecology and management, vol 506 (February-15 2022)PermalinkAn integrated framework of global sensitivity analysis and calibration for spatially explicit agent-based models / Jeon-Young Kang in Transactions in GIS, vol 26 n° 1 (February 2022)PermalinkAn open science and open data approach for the statistically robust estimation of forest disturbance areas / Saverio Francini in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)PermalinkCalibrating GNSS phase biases with onboard observations of low earth orbit satellites / Xingxing Li in Journal of geodesy, vol 96 n° 2 (February 2022)PermalinkEfficient variance component estimation for large-scale least-squares problems in satellite geodesy / Yufeng Nie in Journal of geodesy, vol 96 n° 2 (February 2022)PermalinkEvaluation of mapped-plot variance estimators across a range of partial nonresponse in a post-stratified national forest inventory / James A. Westfall in Canadian Journal of Forest Research, Vol 52 n° 2 (February 2022)PermalinkExploring 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)PermalinkIntegrating terrestrial laser scanning and unmanned aerial vehicle photogrammetry to estimate individual tree attributes in managed coniferous forests in Japan / Katsuto Shimizu in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)PermalinkNovel model for predicting individuals’ movements in dynamic regions of interest / Xiaoqi Shen in GIScience and remote sensing, vol 59 n° 1 (2022)PermalinkSNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows / Qiliang Liu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)PermalinkUsing 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)PermalinkApprentissage de représentations et modèles génératifs profonds dans les systèmes dynamiques / Jean-Yves Franceschi (2022)PermalinkBest 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)PermalinkA constraint-based approach for identifying the urban–rural fringe of polycentric cities using multi-sourced data / Jing Yang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)PermalinkContraintes observationnelles historiques sur la sensibilité climatique : implications pour les projections de la hausse du niveau de la mer / Jonathan Chenal (2022)PermalinkConventional and neural network-based water vapor density model for GNSS troposphere tomography / Chen Liu in GPS solutions, vol 26 n° 1 (January 2022)PermalinkDART: An efficient 3D Monte Carlo vector radiative transfer model for remote sensing applications / Yingjie Wang (2022)PermalinkDetecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation / Guiming Zhang in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)PermalinkEmpirical 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)Permalink