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What, Where, and How to Transfer in SAR Target Recognition Based on Deep CNNs / Zhongling Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2324 - 2336 Titre : What, Where, and How to Transfer in SAR Target Recognition Based on Deep CNNs Type de document : Article/Communication Auteurs : Zhongling Huang, Auteur ; Zongxu Pan, Auteur ; Bin Lei, Auteur Année de publication : 2020 Article en page(s) : pp 2324 - 2336 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection de cible [Termes descripteurs IGN] données multisources [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] source de données [Termes descripteurs IGN] télédétection spatiale [Termes descripteurs IGN] transmission de données Résumé : (auteur) Deep convolutional neural networks (DCNNs) have attracted much attention in remote sensing recently. Compared with the large-scale annotated data set in natural images, the lack of labeled data in remote sensing becomes an obstacle to train a deep network very well, especially in synthetic aperture radar (SAR) image interpretation. Transfer learning provides an effective way to solve this problem by borrowing knowledge from the source task to the target task. In optical remote sensing application, a prevalent mechanism is to fine-tune on an existing model pretrained with a large-scale natural image data set, such as ImageNet. However, this scheme does not achieve satisfactory performance for SAR applications because of the prominent discrepancy between SAR and optical images. In this article, we attempt to discuss three issues that are seldom studied before in detail: 1) what network and source tasks are better to transfer to SAR targets; 2) in which layer are transferred features more generic to SAR targets; and 3) how to transfer effectively to SAR targets recognition. Based on the analysis, a transitive transfer method via multisource data with domain adaptation is proposed in this article to decrease the discrepancy between the source data and SAR targets. Several experiments are conducted on OpenSARShip. The results indicate that the universal conclusions about transfer learning in natural images cannot be completely applied to SAR targets, and the analysis of what and where to transfer in SAR target recognition is helpful to decide how to transfer more effectively. Numéro de notice : A2020-195 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947634 date de publication en ligne : 20/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947634 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94863 [article]

Combining land cover products using a minimum divergence and a Bayesian data fusion approach / Sarah Gengler in International journal of geographical information science IJGIS, vol 32 n° 3-4 (March - April 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 3-4 (March - April 2018) . - pp 806 - 826 Titre : Combining land cover products using a minimum divergence and a Bayesian data fusion approach Type de document : Article/Communication Auteurs : Sarah Gengler, Auteur ; Patrick Bogaert, Auteur Année de publication : 2018 Article en page(s) : pp 806 - 826 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] Belgique [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] distance de Kullback-Leibler [Termes descripteurs IGN] entropie maximale [Termes descripteurs IGN] entropie relative [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] source de données Résumé : (Auteur) Land cover mapping plays an important role for a wide spectrum of applications that are ranging from climate modeling to food security. However, it is a common case that several and partially conflicting land cover products are available at the same time over a same area, where each product suffers from specific limitations and lack of accuracy. In order to take advantage of the best features of each product while at the same time attenuating their respective weaknesses, this paper is proposing a methodology that allows the user to combine these products together based on a general framework involving maximum entropy/minimum divergence principles, Bayesian data fusion and Bayesian updating. First, information brought by each land cover product is coded in terms of inequality constraints so that a first estimation of their quality can be computed based on a maximum entropy/minimum divergence principle. Information from these various land cover products can then be fused afterwards in a Bayesian framework, leading to a single map with an associated measure of uncertainty. Finally, it is shown how the additional information brought by control data can help improving this fused map through a Bayesian updating procedure. The first part of the paper is briefly presenting the most important theoretical results, while the second part is illustrating the use of this suggested approach for a specific area in Belgium, where five different land cover products are at hand. The benefits and limitations of this approach are finally discussed by the light of the results for this case study. Numéro de notice : A2018-045 Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1413577 En ligne : https://doi.org/10.1080/13658816.2017.1413577 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89267 [article]

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Privacy-preserving detection of anomalous phenomena in crowdsourced environmental sensing using fine-grained weighted voting / Mihai Maruseac in Geoinformatica [en ligne], vol 21 n° 4 (October - December 2017)  

Public [article]  inGeoinformatica [en ligne] > vol 21 n° 4 (October - December 2017) . - pp 733 - 762 Titre : Privacy-preserving detection of anomalous phenomena in crowdsourced environmental sensing using fine-grained weighted voting Type de document : Article/Communication Auteurs : Mihai Maruseac, Auteur ; Gabriel Ghinita, Auteur ; Goce Trajcevski, Auteur ; Peter Scheuermann, Auteur Année de publication : 2017 Article en page(s) : pp 733 - 762 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] production participative [Termes descripteurs IGN] protection de la vie privée [Termes descripteurs IGN] sécurité publique [Termes descripteurs IGN] source de données Résumé : (Auteur) This article addresses the problem of preserving privacy of individuals who participate in collaborative environmental sensing. We observe that in many applications of societal importance, one is interested in constructing a map of the spatial distribution of a given phenomenon (e.g., temperature, CO2 concentration, water polluting agents, etc.) and mobile users can contribute with providing measurements data. However, contributing data may leak sensitive private details, as an adversary could infer the presence of a person in a certain location at a given time. This, in turn, may reveal information about other contexts (e.g., health, lifestyle choices), and may even impact an individual’s physical safety. We introduce a technique for privacy-preserving detection of anomalous phenomena, where the privacy of the individuals participating in collaborative environmental sensing is protected according to the powerful semantic model of differential privacy. We propose a differentially-private index structure to address the specific needs of anomalous phenomenon detection and derive privacy preserving query strategies that judiciously allocate the privacy budget to maintain high data accuracy. In addition, we construct an analytical model to characterize the sensed value inaccuracy introduced by the differentially-private noise injection, derive error bounds, and perform a statistical analysis that allows us to improve accuracy by using custom weights for measurements in each cell of the index structure. Extensive experimental results show that the proposed approach achieves high precision in identifying anomalies, and incurs low computational overhead. Numéro de notice : A2017-602 Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-017-0304-3 En ligne : https://doi.org/10.1007/s10707-017-0304-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86910 [article]

Advancing science with VGI: Reproducibility and replicability of recent studies / Franck O. Ostermann in Transactions in GIS, vol 21 n° 2 (April 2017)  

Public [article]  inTransactions in GIS > vol 21 n° 2 (April 2017) . - pp 224 – 237 Titre : Advancing science with VGI: Reproducibility and replicability of recent studies Type de document : Article/Communication Auteurs : Franck O. Ostermann, Auteur ; Carlos Granell, Auteur Année de publication : 2017 Article en page(s) : pp 224 – 237 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] diffusion de données [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] gestion de crise [Termes descripteurs IGN] réplication [Termes descripteurs IGN] source de données [Termes descripteurs IGN] test de performance Résumé : (auteur) In scientific research, reproducibility and replicability are requirements to ensure the advancement of our body of knowledge. This holds true also for VGI-related research and studies. However, the characteristics of VGI suggest particular difficulties in ensuring reproducibility and replicability. In this article, we aim to examine the current situation in VGI-related research, and identify strategies to ensure realization of its full potential. To do so, we first investigate the different aspects of reproducibility and replicability and their impact on VGI-related research. These impacts are different depending on the objectives of the study. Therefore, we examine the study focus of VGI-related research to assess the current body of research and structure our assessment. This work is based on a rigorous review of the elements of reproducibility and a systematic mapping and analysis of 58 papers on the use of VGI in the crisis management field. Results of our investigation show that reproducibility issues related to data are a serious concern, while reproducibility issues related to analysis methods and processes face fewer challenges. However, since most studies still focus on analyzing the source data, reproducibility and replicability are still an unsolved problem in VGI-related research. Therefore, we show initiatives tackling the problem, and finally formulate strategies to improve the situation. Numéro de notice : A2017-164 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1111/tgis.12195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84698 [article]

Crowd and community sourced data quality assessment / Laurence Jolivet (2017)  

Public contenu dans Advances in cartography and GIScience / Michael P. Peterson (2017)   Titre : Crowd and community sourced data quality assessment Type de document : Article/Communication Auteurs : Laurence Jolivet , Auteur ; Ana-Maria Olteanu-Raimond , Auteur Congrès : Congrès: ICC 2017 28th International Cartographic Conference ICA (2 - 7 juillet 2017; Washington DC, Etats-Unis) , Commanditaire Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2017 Collection : Lecture notes in Geoinformation and Cartography, ISSN 1863-2246 Importance : pp 47 - 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] cartographie collaborative [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] évaluation des données [Termes descripteurs IGN] indicateur de qualité [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] source de données Résumé : (auteur) Data quality assessment of different volunteered initiatives and platforms presents several challenges for data validation given the high amount of data collected. This paper focuses on two goals. The first consists in defining both a generic workflow and data quality indicators for validation of reports coming from crowd and community sourcing platforms. In the proposed workflow, a qualified report can be even described by each indicator separately or by a combination of them. Here, we focus mostly on analyzing the results obtained for each indicator separately. The second goal is to learn more information about contributors who has engaged in a platform proposed by a public body (i.e., the French National Mapping Agency): Who are they? How are they contributing? What are their motivations? More is known about contributors to OpenStreetMap than of any other VGI platform. Indeed, knowing the contributors is a crucial task for both motivation and data quality, especially now that public institutions are engaging with VGI. Numéro de notice : C2017-003 Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-319-57336-6_4 date de publication en ligne : 31/05/2017 En ligne : https://doi.org/10.1007/978-3-319-57336-6_4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86073

Modeling spatiotemporal information generation / Simon Scheider in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)  

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Immutably answering Why-Not questions for equivalent conjunctive queries / Nicole Bidoit in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 20 n° 5 (septembre - octobre 2015)  

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Une approche de filtrage d’opinions à base de crédibilité dans un contexte de réseaux sociaux / Lobna Azaza in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 20 n° 4 (juillet - août 2015)  

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Recommendations in location-based social networks: a survey / Jie Bao in Geoinformatica [en ligne], vol 19 n° 3 (July - September 2015)  

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An inference-based framework to manage data provenance in geoscience applications / Mohammad Rezwanul Huq in IEEE Transactions on geoscience and remote sensing, vol 51 n° 11 (November 2013)  

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Geoscience data provenance : An overview / Liping Di in IEEE Transactions on geoscience and remote sensing, vol 51 n° 11 (November 2013)  

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Human computation VGI provenance : Semantic web-based representation and publishing / Irene Celino in IEEE Transactions on geoscience and remote sensing, vol 51 n° 11 (November 2013)  

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Introducing provenance capture into a legacy data system / Helen Conover in IEEE Transactions on geoscience and remote sensing, vol 51 n° 11 (November 2013)  

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Mapping geospatial metadata to open provenance model / Chieh-Chieh Feng in IEEE Transactions on geoscience and remote sensing, vol 51 n° 11 (November 2013)  

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Provenance capture and use in a satellite data processing pipeline / Scott Jensen in IEEE Transactions on geoscience and remote sensing, vol 51 n° 11 (November 2013)  

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