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Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam / Vu Anh Tuan in European journal of remote sensing, vol 54 n° 1 (2021)
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Titre : Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam Type de document : Article/Communication Auteurs : Vu Anh Tuan, Auteur ; Nguyen Hong Quang, Auteur ; le Thi Thu Hang, Auteur Année de publication : 2021 Article en page(s) : pp 13 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande L
[Termes IGN] cartographie des risques
[Termes IGN] crue
[Termes IGN] image ALOS
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] inondation
[Termes IGN] Mekong (fleuve)
[Termes IGN] optimisation spatiale
[Termes IGN] surveillance hydrologique
[Termes IGN] Viet NamRésumé : (auteur) One major characteristic of floods is flood extent. Information on this characteristic is indispensable for flood monitoring. Recently, synthetic aperture radar (SAR) data have been increasing in quality and quantity. This allows more flood studies conducted over large areas regardless of cloud and weather conditions and provides advantages including clear surface water classification based on SAR scattering mechanisms for low values (open water) and high values (inundated vegetation, etc.). However, challenges remain due to sources of uncertainties, such as atmospheric disturbances and vegetation masking parts of water surfaces. Therefore, in this study, we aim to optimize flood mapping processes on flooded vegetation that generated high-value pixels based on a SAR scattering mechanism called double bounce that classifies vegetative flooded water in L-band SAR images. This optimization is nearly impossible using Sentinel-1 scenes. Backscattering of time-series Sentinel-1 and ALOS-2 images acquired for the 2018 and 2019 flood season was analysed, thresholded and hybridized for flood mapping of a study site in the Tam Nong district of the Dong Thap Province of Vietnam. We found that the accuracy of SAR flood maps was improved compared to ground truth data when the SAR-extracted vegetative-flooded plains were considered flooded. Numéro de notice : A2021-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2020.1859340 Date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.1080/22797254.2020.1859340 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97015
in European journal of remote sensing > vol 54 n° 1 (2021) . - pp 13 - 28[article]Room semantics inference using random forest and relational graph convolutional networks: A case study of research building / Xuke Hu in Transactions in GIS, Vol 25 n° 1 (February 2021)
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Titre : Room semantics inference using random forest and relational graph convolutional networks: A case study of research building Type de document : Article/Communication Auteurs : Xuke Hu, Auteur ; Hongchao Fan, Auteur ; Alexey Noskov, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 71 - 111 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage automatique
[Termes IGN] bâtiment public
[Termes IGN] carte d'intérieur
[Termes IGN] cartographie automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] graphe relationnel
[Termes IGN] inférence sémantiqueRésumé : (Auteur) Semantically rich maps are the foundation of indoor location‐based services. Many map providers such as OpenStreetMap and automatic mapping solutions focus on the representation and detection of geometric information (e.g., shape of room) and a few semantics (e.g., stairs and furniture) but neglect room usage. To mitigate the issue, this work proposes a general room tagging method for public buildings, which can benefit both existing map providers and automatic mapping solutions by inferring the missing room usage based on indoor geometric maps. Two kinds of statistical learning‐based room tagging methods are adopted: traditional machine learning (e.g., random forests) and deep learning, specifically relational graph convolutional networks (R‐GCNs), based on the geometric properties (e.g., area), topological relationships (e.g., adjacency and inclusion), and spatial distribution characteristics of rooms. In the machine learning‐based approach, a bidirectional beam search strategy is proposed to deal with the issue that the tag of a room depends on the tag of its neighbors in an undirected room sequence. In the R‐GCN‐based approach, useful properties of neighboring nodes (rooms) in the graph are automatically gathered to classify the nodes. Research buildings are taken as examples to evaluate the proposed approaches based on 130 floor plans with 3,330 rooms by using fivefold cross‐validation. The experiments conducted show that the random forest‐based approach achieves a higher tagging accuracy (0.85) than R‐GCN (0.79). Numéro de notice : A2021-186 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12664 Date de publication en ligne : 19/08/2020 En ligne : https://doi.org/10.1111/tgis.12664 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97152
in Transactions in GIS > Vol 25 n° 1 (February 2021) . - pp 71 - 111[article]Unsupervised deep representation learning for real-time tracking / Ning Wang in International journal of computer vision, vol 129 n° 2 (February 2021)
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Titre : Unsupervised deep representation learning for real-time tracking Type de document : Article/Communication Auteurs : Ning Wang, Auteur ; Wengang Zhou, Auteur ; Yibing Song, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 400 - 418 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] classification non dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de cible
[Termes IGN] filtre
[Termes IGN] objet mobile
[Termes IGN] oculométrie
[Termes IGN] reconnaissance d'objets
[Termes IGN] réseau neuronal siamois
[Termes IGN] temps réel
[Termes IGN] traçage
[Termes IGN] trajectoire (véhicule non spatial)
[Termes IGN] vision par ordinateurRésumé : (auteur) The advancement of visual tracking has continuously been brought by deep learning models. Typically, supervised learning is employed to train these models with expensive labeled data. In order to reduce the workload of manual annotation and learn to track arbitrary objects, we propose an unsupervised learning method for visual tracking. The motivation of our unsupervised learning is that a robust tracker should be effective in bidirectional tracking. Specifically, the tracker is able to forward localize a target object in successive frames and backtrace to its initial position in the first frame. Based on such a motivation, in the training process, we measure the consistency between forward and backward trajectories to learn a robust tracker from scratch merely using unlabeled videos. We build our framework on a Siamese correlation filter network, and propose a multi-frame validation scheme and a cost-sensitive loss to facilitate unsupervised learning. Without bells and whistles, the proposed unsupervised tracker achieves the baseline accuracy of classic fully supervised trackers while achieving a real-time speed. Furthermore, our unsupervised framework exhibits a potential in leveraging more unlabeled or weakly labeled data to further improve the tracking accuracy. Numéro de notice : A2021-353 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s11263-020-01357-4 Date de publication en ligne : 21/09/2020 En ligne : https://doi.org/10.1007/s11263-020-01357-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97604
in International journal of computer vision > vol 129 n° 2 (February 2021) . - pp 400 - 418[article]Web‐based real‐time visualization of large‐scale weather radar data using 3D tiles / Mingyue Lu in Transactions in GIS, Vol 25 n° 1 (February 2021)
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Titre : Web‐based real‐time visualization of large‐scale weather radar data using 3D tiles Type de document : Article/Communication Auteurs : Mingyue Lu, Auteur ; Xinhao Wang, Auteur ; Xintao Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 25 - 43 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Chine
[Termes IGN] dalle
[Termes IGN] données météorologiques
[Termes IGN] données radar
[Termes IGN] géomatique web
[Termes IGN] grande échelle
[Termes IGN] temps réel
[Termes IGN] visualisation 3D
[Termes IGN] web des données
[Termes IGN] WebSIG
[Vedettes matières IGN] GéovisualisationRésumé : (Auteur) Weather radar data play an important role in meteorological analysis and forecasting. In particular, web‐based real‐time 3D visualization will enable and enhance various meteorological applications by avoiding the dissemination of a large amount of data over the internet. Despite that, most existing studies are either limited to 2D or small‐scale data analytics due to methodological limitations. This article proposes a new framework to enable web‐based real‐time 3D visualization of large‐scale weather radar data using 3D tiles and WebGIS technology. The 3D tiles technology is an open specification for online streaming massive heterogeneous 3D geospatial datasets, which is designed to improve rendering performance and reduce memory consumption. First, the weather radar data from multiple single‐radar sites across a large coverage area are organized into a spliced grid data (i.e., weather radar composing data, WRCD). Next, the WRCD is converted into a widely used 3D tile data structure in four steps: data preprocessing, data indexing, data transformation, and 3D tile generation. Last, to validate the feasibility of the proposed strategy, a prototype, namely Meteo3D at https://202.195.237.252:82, is implemented to accommodate the WRCD collected from all the weather radar sites over the whole of China. The results show that near real‐time and accurate visualization for the monitoring and early warning of strong convective weather can be achieved. Numéro de notice : A2021-185 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12638 Date de publication en ligne : 19/05/2020 En ligne : https://doi.org/10.1111/tgis.12638 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97147
in Transactions in GIS > Vol 25 n° 1 (February 2021) . - pp 25 - 43[article]GIS-based multicriteria evaluation for earthquake response: a case study of expert opinion in Vancouver, Canada / Blake Byron Walker in Natural Hazards, Vol 105 n° 2 (January 2021)
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Titre : GIS-based multicriteria evaluation for earthquake response: a case study of expert opinion in Vancouver, Canada Type de document : Article/Communication Auteurs : Blake Byron Walker, Auteur ; Nadine Schuurman, Auteur ; David Swanlund, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2075 - 2091 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] allocation
[Termes IGN] analyse multicritère
[Termes IGN] cartographie collaborative
[Termes IGN] cartographie d'urgence
[Termes IGN] planification urbaine
[Termes IGN] prévention des risques
[Termes IGN] secours d'urgence
[Termes IGN] séisme
[Termes IGN] Vancouver (Colombie britannique)
[Termes IGN] zone à risqueRésumé : (auteur) GIS-based multicriteria evaluation (MCE) provides a framework for analysing complex decision problems by quantifying variables of interest to score potential locations according to their suitability. In the context of earthquake preparedness and post-disaster response, MCE has relied mainly on uninformed or non-expert stakeholders to identify high-risk zones, prioritise areas for response, or highlight vulnerable populations. In this study, we compare uninformed, informed non-expert, and expert stakeholders’ responses in MCE modelling for earthquake response planning in Vancouver, Canada. Using medium- to low-complexity MCE models, we highlight similarities and differences in the importance of infrastructural and socioeconomic variables, emergency services, and liquefaction potential between a non-weighted MCE, a medium-complexity informed non-expert MCE, and a low-complexity MCE informed by 35 local earthquake planning and response experts from governmental and non-governmental organisations. Differences in the observed results underscore the importance of accessible, expert-informed approaches for prioritising locations for earthquake response planning and for the efficient and geographically precise allocation of resources. Numéro de notice : A2021-203 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11069-020-04390-1 Date de publication en ligne : 30/10/2020 En ligne : https://doi.org/10.1007/s11069-020-04390-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97164
in Natural Hazards > Vol 105 n° 2 (January 2021) . - pp 2075 - 2091[article] PermalinkAn attempt to define perceptive and sensitive mapping through lived space experiments / Catherine Dominguès (2021)
PermalinkAssessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin / Luis Felipe Galizia in Natural Hazards and Earth System Sciences, vol 21 n° 1 (January 2021)
PermalinkCalcul de la largeur à pleins bords de grands cours d’eau à partir de MNT LiDAR / Nicolas Fermen (2021)
PermalinkPermalinkCartographie de gîsements de matières colorantes utilisées pendant la Préhistoire et configuration de l’application Input de relevés de terrain / Mathilde Waymel (2021)
PermalinkCartographies en mouvement : parcours sensible, narration et participation, ch. 10. Conception de cartes en relief pour les personnes déficientes visuelles / Gauthier Fillières-Riveau (2021)
PermalinkPermalinkDéveloppement d'un modèle de macro-dynamique forestière pour simuler la dynamique des forêts françaises dans un contexte non-stationnaire / Timothée Audinot (2021)
PermalinkElevation models for reproducible evaluation of terrain representation / Patrick Kennelly in Cartography and Geographic Information Science, vol 48 n° 1 (January 2021)
PermalinkEvaluating interactive comparison techniques in a multiclass density map for visual crime analytics / Lukas Svicarovic (2021)
PermalinkFlood mapping from radar remote sensing using automated image classification techniques / Lisa Landuyt (2021)
PermalinkGeospatial analysis of September, 2019 floods in the lower gangetic plains of Bihar using multi-temporal satellites and river gauge data / C.M. Bhatt in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)
PermalinkHow do people perceive the disclosure risk of maps? Examining the perceived disclosure risk of maps and its implications for geoprivacy protection / Junghwan Kim in Cartography and Geographic Information Science, vol 48 n° 1 (January 2021)
PermalinkA hybrid approach for recovering high-resolution temporal gravity fields from satellite laser ranging / Anno Löcher in Journal of geodesy, vol 95 n° 1 (January 2021)
PermalinkImage matching from handcrafted to deep features: A survey / Jiayi Ma in International journal of computer vision, vol 29 n° 1 (January 2021)
PermalinkInitialization methods of convolutional neural networks for detection of image manipulations / Ivan Castillo Camacho (2021)
PermalinkJahresbericht 2020 des Bundesamtes für Kartographie und Geodäsie / Bundesamt für Kartographie und Geodäsie (2021)
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