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Vehicle detection of multi-source remote sensing data using active fine-tuning network / Xin Wu in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
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Titre : Vehicle detection of multi-source remote sensing data using active fine-tuning network Type de document : Article/Communication Auteurs : Xin Wu, Auteur ; Wei Li, Auteur ; Danfeng Hong, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 39 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] Allemagne
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données multisources
[Termes IGN] image aérienne
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle stéréoscopique
[Termes IGN] segmentation
[Termes IGN] segmentation sémantique
[Termes IGN] véhiculeRésumé : (auteur) Vehicle detection in remote sensing images has attracted increasing interest in recent years. However, its detection ability is limited due to lack of well-annotated samples, especially in densely crowded scenes. Furthermore, since a list of remotely sensed data sources is available, efficient exploitation of useful information from multi-source data for better vehicle detection is challenging. To solve the above issues, a multi-source active fine-tuning vehicle detection (Ms-AFt) framework is proposed, which integrates transfer learning, segmentation, and active classification into a unified framework for auto-labeling and detection. The proposed Ms-AFt employs a fine-tuning network to firstly generate a vehicle training set from an unlabeled dataset. To cope with the diversity of vehicle categories, a multi-source based segmentation branch is then designed to construct additional candidate object sets. The separation of high quality vehicles is realized by a designed attentive classifications network. Finally, all three branches are combined to achieve vehicle detection. Extensive experimental results conducted on two open ISPRS benchmark datasets, namely the Vaihingen village and Potsdam city datasets, demonstrate the superiority and effectiveness of the proposed Ms-AFt for vehicle detection. In addition, the generalization ability of Ms-AFt in dense remote sensing scenes is further verified on stereo aerial imagery of a large camping site. Numéro de notice : A2020-546 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.016 Date de publication en ligne : 13/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95772
in ISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 39 - 53[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience / Yingwei Yan in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)
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Titre : Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience Type de document : Article/Communication Auteurs : Yingwei Yan, Auteur ; Chen-Chieh Feng, Auteur ; Wei Huang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1765 - 1791 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] données localisées des bénévoles
[Termes IGN] GeoWeb
[Termes IGN] littérature
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] presse (media)
[Termes IGN] problème de Dirichlet
[Termes IGN] qualité des données
[Termes IGN] recherche
[Termes IGN] réseau social
[Termes IGN] SIG participatif
[Termes IGN] source de données
[Termes IGN] système d'information géographique
[Termes IGN] Twitter
[Termes IGN] utilisation du sol
[Termes IGN] WikimapiaRésumé : (auteur) More than 10 years have passed since the coining of the term volunteered geographic information (VGI) in 2007. This article presents the results of a review of the literature concerning VGI. A total of 346 articles published in 24 international refereed journals in GIScience between 2007 and 2017 have been reviewed. The review has uncovered varying levels of popularity of VGI research over space and time, and varying interests in various sources of VGI (e.g. OpenStreetMap) and VGI-related terms (e.g. user-generated content) that point to the multi-perspective nature of VGI. Content-wise, using latent Dirichlet allocation (LDA), this study has extracted 50 specific research topics pertinent to VGI. The 50 topics have been subsequently clustered into 13 intermediate topics and three overarching themes to allow a hierarchical topic review. The overarching VGI research themes include (1) VGI contributions and contributors, (2) main fields applying VGI, and (3) conceptions and envisions. The review of the articles under the three themes has revealed the progress and the points that demand attention regarding the individual topics. This article also discusses the areas that the existing research has not yet adequately explored and proposes an agenda for potential future research endeavors. Numéro de notice : A2020-476 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1730848 Date de publication en ligne : 26/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1730848 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95623
in International journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1765 - 1791[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020091 RAB Revue Centre de documentation En réserve L003 Disponible Water level prediction from social media images with a multi-task ranking approach / P. Chaudhary in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
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Titre : Water level prediction from social media images with a multi-task ranking approach Type de document : Article/Communication Auteurs : P. Chaudhary, Auteur ; Stefano D'Aronco, Auteur ; João P. Leitão, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 252 - 262 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] inondation
[Termes IGN] niveau hydrostatique
[Termes IGN] régression
[Termes IGN] réseau social
[Termes IGN] surveillance hydrologique
[Termes IGN] vision par ordinateurRésumé : (auteur) Floods are among the most frequent and catastrophic natural disasters and affect millions of people worldwide. It is important to create accurate flood maps to plan (offline) and conduct (real-time) flood mitigation and flood rescue operations. Arguably, images collected from social media can provide useful information for that task, which would otherwise be unavailable. We introduce a computer vision system that estimates water depth from social media images taken during flooding events, in order to build flood maps in (near) real-time. We propose a multi-task (deep) learning approach, where a model is trained using both a regression and a pairwise ranking loss. Our approach is motivated by the observation that a main bottleneck for image-based flood level estimation is training data: it is difficult and requires a lot of effort to annotate uncontrolled images with the correct water depth. We demonstrate how to efficiently learn a predictor from a small set of annotated water levels and a larger set of weaker annotations that only indicate in which of two images the water level is higher, and are much easier to obtain. Moreover, we provide a new dataset, named DeepFlood, with 8145 annotated ground-level images, and show that the proposed multi-task approach can predict the water level from a single, crowd-sourced image with 11 cm root mean square error. Numéro de notice : A2020-549 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.07.003 Date de publication en ligne : 29/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.07.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95776
in ISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 252 - 262[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
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Titre : X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data Type de document : Article/Communication Auteurs : Danfeng Hong, Auteur ; Naoto Yokoya, Auteur ; Gui-Song Sia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 12 - 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage profond
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] bruit blanc
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compréhension de l'image
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] scène urbaine
[Termes IGN] transmission de donnéesRésumé : (auteur) This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture radar (SAR) data, are openly available on a global scale, enabling parsing global urban scenes through remote sensing imagery. However, their ability in identifying materials (pixel-wise classification) remains limited, due to the noisy collection environment and poor discriminative information as well as limited number of well-annotated training images. To this end, we propose a novel cross-modal deep-learning framework, called X-ModalNet, with three well-designed modules: self-adversarial module, interactive learning module, and label propagation module, by learning to transfer more discriminative information from a small-scale hyperspectral image (HSI) into the classification task using a large-scale MSI or SAR data. Significantly, X-ModalNet generalizes well, owing to propagating labels on an updatable graph constructed by high-level features on the top of the network, yielding semi-supervised cross-modality learning. We evaluate X-ModalNet on two multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a significant improvement in comparison with several state-of-the-art methods. Numéro de notice : A2020-544 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.014 Date de publication en ligne : 11/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95770
in ISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 12 - 23[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Photoperiod and temperature as dominant environmental drivers triggering secondary growth resumption in Northern Hemisphere conifers / Jian-Guo Huang in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 117 n° 34 (August 2020)
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Titre : Photoperiod and temperature as dominant environmental drivers triggering secondary growth resumption in Northern Hemisphere conifers Type de document : Article/Communication Auteurs : Jian-Guo Huang, Auteur ; Qianqian Ma, Auteur ; Sergio Rossi, Auteur ; Franco Biondi, Auteur ; Annie Deslauriers, Auteur ; Patrick Fonti, Auteur ; Eryuan Liang, Auteur ; Harri Mäkinen, Auteur ; et al., Auteur ; Henri E. Cuny , Auteur ; et al., Auteur
Année de publication : 2020 Projets : ARBRE / AgroParisTech (2007 -) Article en page(s) : pp 20645 - 20652 Note générale : bibliographie
This work was funded by the National Natural Science Foundation of China (Grants 41861124001, 41661144007, and 31971499), the International Collaborative Key Project of the Chinese Academy of Sciences (CAS) (Grant GJHZ1752), GuangdongNatural Science Foundation (Grant 2019B121202007), and CAS President’s International Fellowship Initiative (Grant 2019VBA0049). Other funding agencies included the Austrian Science Fund (Grant P22280-B16; GrantP25643-B16), Consortium de Recherche sur la Forêt Boréale Commerciale, Fonds de Recherche sur la Nature et les Technologies du Québec, Forêt d’Enseignement et de Recherche Simon couche, Natural Sciences and Engineering Research Council of Canada, Slovenian Research Agency (Young Researchers’ Program, Programs P4-0015 and P4-0107, and Project Z4-7318), Italian Ministry of Education, University and Research–PRIN 2002(Grant 2002075152) and 2005 (Grant 2005072877), Swiss National Science Foundation (Projects INTEGRAL-121859 and LOTFOR-150205), French National Research Agency (ANR) as part of the “Investissements d’Avenir” program (Grant ANR-11-LABX-0002-01, Laboratory of Excellence for Advanced Research on the Biology of Tree and Forest Ecosystems), Academy of Finland (Grants 250299, 257641, and 265504), National Natural Science Foundation of China (Grant 41525001), Grant Agency of Czech Republic (Grant P504/11/P557), and Provincia Autonoma di Trento (Project “SOFIE 2,”3012/2007). F.B. was supported, in part, by the National Science Foundation under Grant AGS-P2C2-1903561. The cooperation among authors was supported by the European Union Cooperation in Science and Technology Action FP1106STReES.Langues : Anglais (eng) Descripteur : [Termes IGN] formation du bois
[Termes IGN] hémisphère Nord
[Termes IGN] phénologie
[Termes IGN] Pinophyta
[Termes IGN] puits de carbone
[Termes IGN] température au sol
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Wood formation consumes around 15% of the anthropogenic CO2 emissions per year and plays a critical role in long-term sequestration of carbon on Earth. However, the exogenous factors driving wood formation onset and the underlying cellular mechanisms are still poorly understood and quantified, and this hampers an effective assessment of terrestrial forest productivity and carbon budget under global warming. Here, we used an extensive collection of unique datasets of weekly xylem tissue formation (wood formation) from 21 coniferous species across the Northern Hemisphere (latitudes 23 to 67°N) to present a quantitative demonstration that the onset of wood formation in Northern Hemisphere conifers is primarily driven by photoperiod and mean annual temperature (MAT), and only secondarily by spring forcing, winter chilling, and moisture availability. Photoperiod interacts with MAT and plays the dominant role in regulating the onset of secondary meristem growth, contrary to its as-yet-unquantified role in affecting the springtime phenology of primary meristems. The unique relationships between exogenous factors and wood formation could help to predict how forest ecosystems respond and adapt to climate warming and could provide a better understanding of the feedback occurring between vegetation and climate that is mediated by phenology. Our study quantifies the role of major environmental drivers for incorporation into state-of-the-art Earth system models (ESMs), thereby providing an improved assessment of long-term and high-resolution observations of biogeochemical cycles across terrestrial biomes. Numéro de notice : A2020-329 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1073/pnas.2007058117 En ligne : https://doi.org/10.1073/pnas.2007058117 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96865
in Proceedings of the National Academy of Sciences of the United States of America PNAS > vol 117 n° 34 (August 2020) . - pp 20645 - 20652[article]vol V-4-2020 - August 2020 - [Actes] XXIV ISPRS virtual Congress, Commission 4, 31th August-2nd September 2020, Nice, France (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Nicolas Paparoditis
PermalinkAccuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets / Lamin R. Mansaray in Geocarto international, vol 35 n° 10 ([01/08/2020])
Permalinkvol V-3-2020 - August 2020 - [Actes] XXIV ISPRS virtual Congress, Commission 3, 31th August-2nd September 2020, Nice, France (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Nicolas Paparoditis
PermalinkAn assessment of wide-lane ambiguity resolution methods for multi-frequency multi-GNSS precise point positioning / Viet Duong in Survey review, vol 52 n° 374 (August 2020)
PermalinkCan ensemble techniques improve coral reef habitat classification accuracy using multispectral data? / Mohammad Shawkat Hossain in Geocarto international, vol 35 n° 11 ([01/08/2020])
PermalinkConjugate ruptures and seismotectonic implications of the 2019 Mindanao earthquake sequence inferred from Sentinel-1 InSAR data / Bingquan Li in International journal of applied Earth observation and geoinformation, vol 90 (August 2020)
PermalinkDevelopment and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping / Alvin B. Baloloy in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
PermalinkEstimates of spaceborne precipitation radar pulsewidth and beamwidth using sea surface echo data / Kaya Kanemaru in IEEE Transactions on geoscience and remote sensing, vol 58 n° 8 (August 2020)
PermalinkEvaluation of single-frequency receivers for studying crustal deformation at the longitudinal Valley fault, eastern Taiwan / Horng-Yue Chen in Survey review, vol 52 n° 374 (August 2020)
PermalinkGeneration of crowd arrival and destination locations/times in complex transit facilities / Brian Ricks in The Visual Computer, vol 36 n° 8 (August 2020)
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