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Coastline change modelling induced by climate change using geospatial techniques in Togo (West Africa) / Yawo Konko in Advances in Remote Sensing, vol 9 n° 2 (June 2020)
[article]
Titre : Coastline change modelling induced by climate change using geospatial techniques in Togo (West Africa) Type de document : Article/Communication Auteurs : Yawo Konko, Auteur ; Appollonia Okhimambe, Auteur ; Pouwèréou Nimon, Auteur ; Jerry Asaana, Auteur ; Jean-Paul Rudant , Auteur ; Kouami Kokou, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 85 - 100 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] changement climatique
[Termes IGN] classification non dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données multisources
[Termes IGN] érosion côtière
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Sentinel-MSI
[Termes IGN] niveau de la mer
[Termes IGN] Normalized Difference Water Index
[Termes IGN] outil d'aide à la décision
[Termes IGN] régression linéaire
[Termes IGN] série temporelle
[Termes IGN] surveillance du littoral
[Termes IGN] Togo
[Termes IGN] trait de côteRésumé : (auteur) Climate change is a major concern of humanity. One of the consequences of climate change is global warming causing melting glaciers, rising sea levels and shoreline regression. In Togo, the regression of shoreline leads to coastal erosion with significant damage on socio-economic infrastructures and human habitats. This research, basing on geospatial techniques, focuses on coastal erosion monitoring from 1988 to 2018 in Togo. It is interested in the extraction of shoreline and in the analysis of change. Various satellite images indexes have been developed for shoreline extraction but the major scientific problem concerns the precision of the different classification algorithms methods used for the extraction of the shoreline from these water index. This study used NDWI index from multisource satellite images. It assesses the performance of Otsu threshold segmentation, Iso Cluster Unsupervised Classification and Support Vector Machine (SVM) Supervised Classification methods for the extraction of the shoreline on NDWI index. The topographic morphology such as linear and non-linear coastal surfaces have been considered. The estimation of the rates of change of the shoreline was performed using the statistical linear regression method (LRR). The results revealed that the SVM Supervised Classification method showed good performance on linear and non-linear coastal surface than the other methods. For the kinematics of the shoreline, the southwest of the Togolese coast has an average erosion rate ranging from 2.49 to 5.07 m per year. The results obtained will serve as decision-making support tools for the design and implementation of appropriate adaptations plans to avoid the immersion of the asphalt road by sea, displacement of population and disturbance of human habitats. Numéro de notice : A2020-795 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.4236/ars.2020.92005 Date de publication en ligne : 08/06/2020 En ligne : https://doi.org/10.4236/ars.2020.92005 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96622
in Advances in Remote Sensing > vol 9 n° 2 (June 2020) . - pp 85 - 100[article]Géodésie de poche : toute la géodésie dans votre main / Gilles Canaud in XYZ, n° 163 (juin 2020)
[article]
Titre : Géodésie de poche : toute la géodésie dans votre main Type de document : Article/Communication Auteurs : Gilles Canaud, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 19 - 19 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] borne géodésique
[Termes IGN] espace partagé de travail en ligne
[Termes IGN] interface mobile
[Termes IGN] interopérabilité
[Termes IGN] nivellement direct
[Termes IGN] réseau géodésique
[Termes IGN] téléphone intelligentRésumé : (Auteur) Où que l'on soit, on ne risque plus aujourd'hui de perdre ses repères : la géodésie et le nivellement s'invitent maintenant dans nos téléphones Android et IOS. Outre les bornes et repères de l'IGN, l'application "Géodésie de poche" pour smartphone donne accès aux réseaux des partenaires (CANEX) de l'institut, portant ainsi le nombre d'informations de géodésie à 200.000 et celles du nivellement à plus de 380.000, disponibles à présent en tout temps, par tout temps, en tout lieu. Numéro de notice : A2020-386 Affiliation des auteurs : IGN (2012-2019) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95478
in XYZ > n° 163 (juin 2020) . - pp 19 - 19[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 112-2020021 RAB Revue Centre de documentation En réserve L003 Disponible Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning / Yann Méneroux in International Journal of Data Science and Analytics JDSA, vol 10 n° 1 (June 2020)
[article]
Titre : Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning Type de document : Article/Communication Auteurs : Yann Méneroux , Auteur ; Arnaud Le Guilcher , Auteur ; Guillaume Saint Pierre, Auteur ; Mohammad Ghasemi Hamed, Auteur ; Sébastien Mustière , Auteur ; Olivier Orfila, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 101 - 119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse fonctionnelle (mathématiques)
[Termes IGN] apprentissage profond
[Termes IGN] carte routière
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] détection d'objet
[Termes IGN] données routières
[Termes IGN] feu de circulation
[Termes IGN] inférence
[Termes IGN] reconnaissance de formes
[Termes IGN] signalisation routière
[Termes IGN] trace GPS
[Termes IGN] trafic routier
[Termes IGN] transformation en ondelettes
[Termes IGN] vitesseRésumé : (auteur) The increasing availability of large-scale global positioning system data stemming from in-vehicle-embedded terminal devices enables the design of methods deriving road network cartographic information from drivers’ recorded traces. Some machine learning approaches have been proposed in the past to train automatic road network map inference, and recently this approach has been successfully extended to infer road attributes as well, such as speed limitation or number of lanes. In this paper, we address the problem of detecting traffic signals from a set of vehicle speed profiles, under a classification perspective. Each data instance is a speed versus distance plot depicting over a hundred profiles on a 100-m-long road span. We proposed three different ways of deriving features: The first one relies on the raw speed measurements; the second one uses image recognition techniques; and the third one is based on functional data analysis. We input them into most commonly used classification algorithms, and a comparative analysis demonstrated that a functional description of speed profiles with wavelet transforms seems to outperform the other approaches with most of the tested classifiers. It also highlighted that random forests yield an accurate detection of traffic signals, regardless of the chosen feature extraction method, while keeping a remarkably low confusion rate with stop signs. Numéro de notice : A2020-336 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41060-019-00197-x Date de publication en ligne : 04/10/2019 En ligne : https://doi.org/10.1007/s41060-019-00197-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93755
in International Journal of Data Science and Analytics JDSA > vol 10 n° 1 (June 2020) . - pp 101 - 119[article]Documents numériques
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Traffic signal detection ... - preprintAdobe Acrobat PDF Deep learning for enrichment of vector spatial databases: Application to highway interchange / Guillaume Touya in ACM Transactions on spatial algorithms and systems, TOSAS, vol 6 n° 3 (May 2020)
[article]
Titre : Deep learning for enrichment of vector spatial databases: Application to highway interchange Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Imran Lokhat , Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] base de données vectorielles
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] échangeur routier
[Termes IGN] enrichissement sémantique
[Termes IGN] reconnaissance d'objets
[Termes IGN] segmentation d'imageRésumé : (auteur) Spatial analysis and pattern recognition with vector spatial data is particularly useful to enrich raw data. In road networks, for instance, there are many patterns and structures that are implicit with only road line features, among which highway interchange appeared very complex to recognize with vector-based techniques. The goal is to find the roads that belong to an interchange, such as the slip roads and the highway roads connected to the slip roads. To go further than state-of-the-art vector-based techniques, this article proposes to use raster-based deep learning techniques to recognize highway interchanges. The contribution of this work is to study how to optimally convert vector data into small images suitable for state-of-the-art deep learning models. Image classification with a convolutional neural network (i.e., is there an interchange in this image or not?) and image segmentation with a u-net (i.e., find the pixels that cover the interchange) are experimented and give better results than existing vector-based techniques in this specific use case (99.5% against 74%). Numéro de notice : A2020-365 Affiliation des auteurs : LASTIG COGIT (2012-2019) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1145/3382080 Date de publication en ligne : 01/04/2020 En ligne : https://doi.org/10.1145/3382080 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95399
in ACM Transactions on spatial algorithms and systems, TOSAS > vol 6 n° 3 (May 2020) . - 21 p.[article]Documents numériques
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Deep learning for enrichment of vector spatial databases ... - preprintAdobe Acrobat PDF Exploring the potential of deep learning segmentation for mountain roads generalisation / Azelle Courtial in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)
[article]
Titre : Exploring the potential of deep learning segmentation for mountain roads generalisation Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Achraf El Ayedi, Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : n° 338 ; 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] 1:25.000
[Termes IGN] 1:250.000
[Termes IGN] Alpes (France)
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données routières
[Termes IGN] données vectorielles
[Termes IGN] généralisation automatique de données
[Termes IGN] montagne
[Termes IGN] route
[Termes IGN] segmentation
[Termes IGN] symbole graphique
[Termes IGN] virage
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Among cartographic generalisation problems, the generalisation of sinuous bends in mountain roads has always been a popular one due to its difficulty. Recent research showed the potential of deep learning techniques to overcome some remaining research problems regarding the automation of cartographic generalisation. This paper explores this potential on the popular mountain road generalisation problem, which requires smoothing the road, enlarging the bend summits, and schematising the bend series by removing some of the bends. We modelled the mountain road generalisation as a deep learning problem by generating an image from input vector road data, and tried to generate it as an output of the model a new image of the generalised roads. Similarly to previous studies on building generalisation, we used a U-Net architecture to generate the generalised image from the ungeneralised image. The deep learning model was trained and evaluated on a dataset composed of roads in the Alps extracted from IGN (the French national mapping agency) maps at 1:250,000 (output) and 1:25,000 (input) scale. The results are encouraging as the output image looks like a generalised version of the roads and the accuracy of pixel segmentation is around 65%. The model learns how to smooth the output roads, and that it needs to displace and enlarge symbols but does not always correctly achieve these operations. This article shows the ability of deep learning to understand and manage the geographic information for generalisation, but also highlights challenges to come. Numéro de notice : A2020-295 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050338 Date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.3390/ijgi9050338 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95131
in ISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - n° 338 ; 21 p.[article]La croissance des forêts et les changements environnementaux / François Lebourgeois in Sciences, eaux & territoires, n° 33 (avril 2020)PermalinkL’inventaire forestier national pour un suivi permanent, multi-échelles et multi-thématiques de la forêt française et des ressources bois mobilisables / Antoine Colin in Sciences, eaux & territoires, n° 33 (avril 2020)PermalinkPermalinkA breakpoint detection in the mean model with heterogeneous variance on fixed time-intervals / Olivier Bock in Statistics and Computing, vol 29 n° 1 (February 2020)PermalinkA two-step approach for the correction of rolling shutter distortion in UAV photogrammetry / Yilin Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)PermalinkLa biodiversité à l’épreuve des choix d’aménagement : une approche par la modélisation appliquée à la Région Occitanie / Coralie Calvet in Sciences, eaux & territoires, n° 31 (janvier 2020)PermalinkComparing supervised learning algorithms for Spatial Nominal Entity recognition / Amine Medad (2020)PermalinkConstraint based evaluation of generalized images generated by deep learning / Azelle Courtial (2020)PermalinkDescription and evaluation of DTRF2014, JTRF2014 and ITRF2014, ch. 3. ITRS Center evaluation of DTRF2014 and JTRF2014 with respect to ITRF2014 / Zuheir Altamimi (2020)PermalinkPermalinkGénération de cartes tactiles photoréalistes pour personnes déficientes visuelles par apprentissage profond / Gauthier Fillières-Riveau in Revue internationale de géomatique, vol 30 n° 1-2 (janvier - juin 2020)PermalinkGeographies of maritime transport, Ch. 4. Geography versus topology in the evolution of the global container shipping network (1977-2016) / César Ducruet (2020)PermalinkPermalinkImpact of thermospheric mass density on the orbit prediction of LEO satellites / Changyong He in Space weather, vol 18 n° 1 (January 2020)PermalinkIWV retrieval from ground and shipborne GPS receivers during NAWDEX [diaporama] / Pierre Bosser (2020)PermalinkIWV retrieval from shipborne GPS receiver on hydrographic ship Borda [diaporama] / Olivier Bock (2020)PermalinkMapGenOnto: A shared ontology for map generalisation and multi-scale visualisation / Guillaume Touya (2020)PermalinkPermalinkLa modélisation en géographie : villes et territoires, ch. 4. Modélisation territoriale incrémentale / Clémentine Cottineau (2020)PermalinkOn the joint exploitation of optical and SAR satellite imagery for grassland monitoring / Anatol Garioud (2020)PermalinkPorting ardupilot to ESP32: towards a universal open-source architecture for agile and easily replicable multi-domains mapping robots / Laurent Beaudoin (2020)PermalinkPosition, navigation, and timing technologies in the 21st century: Integrated satellite navigation, sensor systems, and civil applications, ch. 27. Global geodesy and reference frames / Chris Rizos (2020)PermalinkSatellite image time series classification with pixel-set encoders and temporal self-attention / Vivien Sainte Fare Garnot (2020)PermalinkSeeing the trees in the world’s forests: An extension of the forest transition concept / Jean-Daniel Bontemps (2020)PermalinkSimulation and analysis of photogrammetric UAV image blocks - Influence of camera calibration error / Yilin Zhou in Remote sensing, vol 12 n° 1 (January 2020)PermalinkSurveillance de santé structurale des ouvrages d'art incluant les systèmes de positionnement par satellites / Nicolas Manzini (2020)PermalinkPermalinkLe vandalisme de l'information géographique volontaire : analyse exploratoire et proposition d'une méthodologie de détection automatique / Quy Thy Truong (2020)PermalinkVery high resolution land cover mapping of urban areas at global scale with convolutional neural network / Thomas Tilak (2020)PermalinkDesigning geovisual analytics environments and displays with humans in mind / Arzu Çöltekin in ISPRS International journal of geo-information, vol 8 n° 12 (December 2019)PermalinkA learning approach to evaluate the quality of 3D city models / Oussama Ennafii in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 12 (December 2019)PermalinkNew method for environmental monitoring in armed conflict zones: a case study of Syria / Samira Mobaied in Environmental Monitoring and Assessment, vol 191 n° 11 (November 2019)PermalinkSpace test of the Equivalence Principle: first results of the MICROSCOPE mission / Pierre Touboul in Classical and Quantum Gravity, vol 36 n° 22 (November 2019)PermalinkCaractériser et suivre qualitativement et quantitativement les haies et le bocage en France / Sophie Morin in Sciences, eaux & territoires, n° 30 (octobre 2019)PermalinkA factor model approach for the joint segmentation with between‐series correlation / Xavier Collilieux in Scandinavian Journal of Statistics, vol 46 n° 3 (September 2019)PermalinkA filtering-based approach for improving crowdsourced GNSS traces in a data update context / Stefan Ivanovic in ISPRS International journal of geo-information, vol 8 n° 9 (September 2019)PermalinkDiptera in clear-felling stumps like it dry / Mats Jonsell in Scandinavian journal of forest research, vol 34 n° 8 (August 2019)PermalinkAnalysis of collaboration networks in OpenStreetMap through weighted social multigraph mining / Quy Thy Truong in International journal of geographical information science IJGIS, vol 33 n° 7 - 8 (July - August 2019)PermalinkFluorination renders the wood surface hydrophobic without any loss of physical and mechanical properties / Martial Pouzet in Industrial Crops and Products, vol 133 (July 2019)PermalinkIs deep learning the new agent for map generalization? / Guillaume Touya in International journal of cartography, vol 5 n° 2-3 (July - November 2019)PermalinkLettre : Existe-t-il des relations formelles entre coefficients de diffusion radar et facteurs de réflectance en optique ? / Jean-Paul Rudant in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)PermalinkTélédétection radar : de l'image d'intensité initiale au choix du mode de calibration des coefficients de diffusion / Jean-Paul Rudant in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)PermalinkPiecewise-planar approximation of large 3D data as graph-structured optimization / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W5 (May 2019)PermalinkSimulation and analysis of photogrammetric UAV image blocks: influence of camera calibration error / Yilin Zhou in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W5 (May 2019)PermalinkBackground mortality drivers of European tree species: climate change matters / Adrien Taccoen in Proceedings of the Royal society B : Biological sciences, Vol 286 n° 1900 (April 2019)PermalinkDe la carte de Cassini à la géoplateforme de l’État / Daniel Bursaux in Responsabilité et environnement, n° 94 (Avril 2019)PermalinkLe réseau GPS permanent (RGP) de l'IGN / Sébastien Saur in Géomètre, n° 2168 (avril 2019)PermalinkAutomatic derivation of on-demand tactile maps for visually impaired people: first experiments and research agenda / Guillaume Touya in International journal of cartography, vol 5 n° 1 (March 2019)PermalinkLe nivellement de Saint-Germain-en-Laye / Alain Coulomb in XYZ, n° 158 (mars 2019)PermalinkUtilisation d’infrastructures géodésiques mondiales pour la réalisation nationale / Raphaël Legouge in XYZ, n° 158 (mars 2019)PermalinkImprovement of photogrammetric accuracy by modeling and correcting the thermal effect on camera calibration / Mehdi Daakir in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkNear real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors / Pauline Perbet in International Journal of Remote Sensing IJRS, vol 40 n°19 (February 2019)PermalinkThe orthographic projection model for pose calibration of long focal images / Laura F. Julià in IPOL Journal, Image Processing On Line, vol 9 (2019)PermalinkAnalysis and modelling of remote sensing reflectance during anoxic crisis in the Thau lagoon using satellite images / Manchun Lei (2019)PermalinkPermalinkPermalinkCorrelated atom accelerometers for mapping the Earth gravity field from space / Thomas Lévèque (2019)PermalinkData linking by indirect spatial referencing systems, [report of] EuroSDR - EuroGeographics seminar, September 5th - 6th, 2018 - Paris, France / Bénédicte Bucher (2019)PermalinkDétection et localisation d'objets 3D par apprentissage profond en topologie capteur / Pierre Biasutti (2019)PermalinkPermalinkDPOD2014 : A new DORIS extension of ITRF2014 for precise orbit determination / Guilhem Moreaux in Advances in space research, vol 63 n° 1 (1 January 2019)PermalinkPermalinkEnrichissement d'orthophotographie par des données OpenStreetMap pour l'apprentissage machine / Gauthier Fillières-Riveau (2019)PermalinkPermalinkFifty shades of Roboto: text design choices and categories in multi-scale maps / Sébastien Biniek (2019)PermalinkPermalinkPermalinkGeographic Information Systems in Geospatial Intelligence, ch. 5. Spectral optimization of airborne multispectral camera for land cover classification: automatic feature selection and spectral band clustering / Arnaud Le Bris (2019)PermalinkLU-Net, An efficient network for 3D LiDAR point cloud semantic segmentation based on end-to-end-learned 3D features and U-Net / Pierre Biasutti (2019)PermalinkMass variation observing system by high low inter-satellite links (MOBILE) : a new concept for sustained observation of mass transport from space / Roland Pail in Journal of geodetic science, vol 9 n° 1 (January 2019)PermalinkPermalinkReconciling upper mantle seismic velocity and density structure below ocean basins / Isabelle Panet (2019)PermalinkPermalinkSUMAC 2019, 1st workshop on Structuring and Understanding of Multimedia heritAge Contents / Valérie Gouet-Brunet (2019)PermalinkPermalinkThe necessary yet complex evaluation of 3D city models: a semantic approach / Oussama Ennafii (2019)PermalinkPermalinkTime-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series / Vivien Sainte Fare Garnot (2019)PermalinkTowards improving knowledge capitalization system for sport events legacy / Malika Grim-Yefsah (2019)PermalinkLe vandalisme dans l’information géographique volontaire, détection de l’IG volontaire vandalisée : du concept à la détection non supervisée d’anomalie / Quy Thy Truong in Revue internationale de géomatique, vol 29 n° 1 (janvier - mars 2019)PermalinkLa forme de la terre dans l'histoire occidentale / Xavier Della Chiesa in XYZ, n° 157 (décembre 2018 - février 2019)PermalinkThe reviewing process for ISPRS events / Clément Mallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-5 (November 2018)PermalinkLa filiera foresta-legno francese tra potenziale di mitigazione dei cambiamenti climatici e necessità di adattamento / Philippe Delacote in Agriregionieuropa, anno 14 n° 54 (2018)PermalinkPermalinkUnmixing polarimetric radar images based on land cover type identified by higher resolution optical data before target decomposition: application to forest and bare soil / Sébastien Giordano in IEEE Transactions on geoscience and remote sensing, vol 56 n° 10 (October 2018)Permalink3D urban geovisualization: in situ augmented and mixed reality experiments / Alexandre Devaux in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-4 (October 2018)PermalinkGNSS-assisted integrated sensor orientation with sensor pre-calibration for accurate corridor mapping / Yilin Zhou in Sensors, vol 18 n° 9 (September 2018)PermalinkA quelles altitudes se trouvent les horloges atomiques de l'observatoire de Paris ? / Xavier Collilieux in XYZ, n° 156 (septembre - novembre 2018)PermalinkEst-il possible de tirer des enseignements des introductions anciennes d'agents pathogènes ? L'exemple de la graphiose de l'orme / Dominique Piou in Revue forestière française, vol 70 n° 6 (2018)PermalinkForeword to the special issue on urban remote sensing for smarter cities / Prashanth Reddy Marpu in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 11 n° 8 (August 2018)PermalinkUnsupervised detection of ruptures in spatial relationships in video sequences based on log‑likelihood ratio / Abdalbassir Abou-Elailah in Pattern Analysis and Applications, vol 21 n° 3 (August 2018)PermalinkFrom hierarchy to networking: the evolution of the “twenty-first-century Maritime Silk Road” container shipping system / Liehui Wang in Transport reviews, vol 38 n° 4 ([01/07/2018])PermalinkSecond iteration of photogrammetric processing to refine image orientation with improved tie-points / Truong Giang Nguyen in Sensors, vol 18 n° 7 (July 2018)PermalinkSoil moisture estimation in Ferlo region (Senegal) using radar (ENVISAT/ASAR) and optical (SPOT/VEGETATION) data / Gayane Faye in The Egyptian Journal of Remote Sensing and Space Science, Vol. 21 suppl.1 (juillet 2018)PermalinkClassification à très large échelle d’images satellites à très haute résolution spatiale par réseaux de neurones convolutifs / Tristan Postadjian in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)Permalink