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Titre : Spationomy : Spatial exploration of economic data and methods of interdisciplinary analytics Type de document : Guide/Manuel Auteurs : Vit Pászto, Éditeur scientifique ; Carsten Jürgens, Éditeur scientifique ; Polona Tominc, Éditeur scientifique ; et al., Auteur Editeur : Springer Nature Année de publication : 2020 Importance : 333p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-26626-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] acquisition de données
[Termes IGN] analyse multicritère
[Termes IGN] analyse spatiale
[Termes IGN] données socio-économiques
[Termes IGN] exploration de données géographiques
[Termes IGN] finance
[Termes IGN] géographie économique
[Termes IGN] microéconomie
[Termes IGN] modèle conceptuel de données localisées
[Termes IGN] recherche interdisciplinaire
[Termes IGN] régression logistique
[Termes IGN] système d'information géographique
[Termes IGN] veille économiqueRésumé : (éditeur) This open access book is based on 'Spationomy – Spatial Exploration of Economic Data', an interdisciplinary and international project in the frame of ERASMUS+ funded by the European Union. The project aims to exchange interdisciplinary knowledge in the fields of economics and geomatics. For the newly introduced courses, interdisciplinary learning materials have been developed by a team of lecturers from four different universities in three countries. In a first study block, students were taught methods from the two main research fields. Afterwards, the knowledge gained had to be applied in a project. For this international project, teams were formed, consisting of one student from each university participating in the project. The achieved results were presented in a summer school a few months later. At this event, more methodological knowledge was imparted to prepare students for a final simulation game about spatial and economic decision making. In a broader sense, the chapters will present the methodological background of the project, give case studies and show how visualisation and the simulation game works. Numéro de notice : 25973 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Manuel de cours DOI : 10.1007/978-3-030-26626-4 En ligne : https://doi.org/10.1007/978-3-030-26626-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96629 Le temps dans la géolocalisation par satellites / Sébastien Trilles (2020)
Titre : Le temps dans la géolocalisation par satellites Type de document : Monographie Auteurs : Sébastien Trilles, Auteur ; Pierre Spagnou, Auteur Editeur : Paris, Toulouse, ... : Centre national de la recherche scientifique CNRS Année de publication : 2020 Autre Editeur : Les Ulis : EDP Sciences Collection : Savoirs actuels Sous-collection : Physique Importance : 420 p. ISBN/ISSN/EAN : 978-2-7598-2434-2 Note générale : Glossaire et bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] données GNSS
[Termes IGN] échelle de temps
[Termes IGN] effet Doppler
[Termes IGN] espace-temps
[Termes IGN] force de gravitation
[Termes IGN] géolocalisation
[Termes IGN] horloge atomique
[Termes IGN] méthode des moindres carrés
[Termes IGN] orbitographie
[Termes IGN] positionnement par Galileo
[Termes IGN] positionnement par GNSS
[Termes IGN] positionnement par GPS
[Termes IGN] propagation ionosphérique
[Termes IGN] propagation troposphérique
[Termes IGN] relativité générale
[Termes IGN] relativité restreinte
[Termes IGN] théorie de la relativitéIndex. décimale : 30.60 Géodésie spatiale Résumé : (Editeur) Cet ouvrage présente et détaille l'algorithmique la plus récente intervenant dans l'estimation de la position d'un récepteur tout en exposant le plus clairement possible la riche thématique associée au temps. Le temps physique est au coeur de tout système de géolocalisation par satellites. Il n'est donc pas surprenant que la relativité, qui bouleverse les notions habituelles d'espace et de temps, y joue un rôle crucial mais ce qui était peut-être moins attendu est que l'amplitude de certains effets relativistes est considérable à l'échelle de précision requise, même si leur omniprésence échappe à notre perception immédiate. Cet ouvrage fournit aux ingénieurs et physiciens les éléments algorithmiques principaux nécessaires au fonctionnement de tels systèmes sans omettre certains aspects délicats. Les auteurs ont su marier de façon originale deux expertises : l'algorithmique subtile dédiée à la géolocalisation par satellites et la compréhension fine des effets physiques relativistes concernant le temps. Note de contenu :
1. La mesure du temps
2. Les signaux et messages des systèmes GPS et Galileo
3. La mesure du code
4. La mesure de Doppler
5. La mesure de phase
6. Les effets des erreurs système sur les mesures GNSS
7. Les effets de propagations dans l'atmosphère sur les mesures GNSS
8. Les différentes combinaisons de mesures GNSS
9. La diffusion des biais d'horloge satellite dans le message de navigation
10. Les références d'espaces
11. Positionnement avec le système GPS
12. Positionnement en combinant les système GPS et Galileo
13. La théorie de la relativité restreinte
14. Les nouveaux effets physiques sur le temps prédits par la relativité restreinte
15. La théorie de la gravitation de Newton
16. La théorie de la gravitation d'Einstein
17. Les nouveaux effets physiques sur le temps prédits par la relativité générale
18. Les expériences sur la désynchronisation des horloges parfaites
19. Effets relativistes sur le temps pour la géolocalisation par satellites
20. Transfert de temps et transfert de fréquence
21. Principes généraux de la restitution d'orbite GPS par moindres carrés
22. Les systèmes d'augmentation par satellitesNuméro de notice : 26549 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Monographie Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97840 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 26549-01 30.60 Livre Centre de documentation Géodésie Disponible Knowing is not enough: exploring the missing link between climate change knowledge and action of German forest owners and managers / Yvonne Hengst-Ehrhart in Annals of Forest Science, Vol 76 n° 4 (December 2019)
[article]
Titre : Knowing is not enough: exploring the missing link between climate change knowledge and action of German forest owners and managers Type de document : Article/Communication Auteurs : Yvonne Hengst-Ehrhart, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Allemagne
[Termes IGN] foresterie
[Termes IGN] gestion forestière
[Termes IGN] industrie forestière
[Termes IGN] politique forestière
[Termes IGN] propriétaire forestier
[Termes IGN] régression multiple
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Key message : Adaptation to climate change is a complex but urgent task in forest management; however, a lack of action is widely reported. This study shows that adaptive action on both stand and business levels is missing in forest management. Beyond the cognitive dimension, affective and conative aspects should be promoted through awareness-raising initiatives specific to different target groups. Context : Adaptation to climate change is a complex but urgent task in forest management. A lack of action is widely reported combined with a call for awareness-raising and better knowledge transfer to bridge the gap between knowledge and action. Aims : Based on an understanding of awareness encompassing cognitive, affective, and conative dimensions, the paper aims to clarify (1) what kind of adaptive measures are missing in forest management and (2) if there is a gap in climate change awareness of forest owners and managers hindering adaptive action. Methods : An online survey among German forest owners and managers was conducted. The theory of planned behavior was selected to examine variables which support the implementation of adaptive measures and to examine different awareness dimensions. Data was analyzed using descriptive statistics and multiple linear regression analysis. Results : Adaptive measures on stand level were more often implemented than those on business level. All awareness dimensions were influential for the intention to implement adaptive measures. Experience and attitude towards adaptive measures were most important while social norm and perceived behavioral control were influential in some groups. Conclusion : The potential of adaptive measures on stand level and particularly on business level is not fully exploited. Based on these findings, awareness-raising initiatives and forest consultancy can be adapted to consider the specific perspectives of target groups as a means of promoting the implementation of adaptive measures. Numéro de notice : A2019-532 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-019-0878-z Date de publication en ligne : 16/10/2019 En ligne : https://doi.org/10.1007/s13595-019-0878-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94120
in Annals of Forest Science > Vol 76 n° 4 (December 2019)[article]Sig-NMS-based faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery / Ruchan Dong in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)
[article]
Titre : Sig-NMS-based faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery Type de document : Article/Communication Auteurs : Ruchan Dong, Auteur ; Dazhuan Xu, Auteur ; Jin Zhao, Auteur ; Licheng Jiao, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 8534 - 8545 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection d'objet
[Termes IGN] détection de cible
[Termes IGN] image à très haute résolution
[Termes IGN] régression
[Termes IGN] zone d'intérêtRésumé : (auteur) Small target detection is a challenging task in veryhigh-resolution (VHR) optical remote sensing imagery, because small targets occupy a minuscule number of pixels and are easily disturbed by backgrounds or occluded by others. Although current convolutional neural network (CNN)-based approaches perform well when detecting normal objects, they are barely suitable for detecting small ones. Two practical problems stand in their way. First, current CNN-based approaches are not specifically designed for the minuscule size of small targets (~15 or ~10 pixels in extent). Second, no well-established data sets include labeled small targets and establishing one from scratch is labor-intensive and time-consuming. To address these two issues, we propose an approach that combines Sig-NMS-based Faster R-CNN with transfer learning. Sig-NMS replaces traditional non-maximum suppression (NMS) in the stage of region proposal network and decreases the possibility of missing small targets. Transfer learning can effectively label remote sensing images by automatically annotating both object classes and object locations. We conduct an experiment on three data sets of VHR optical remote sensing images, RSOD, LEVIR, and NWPU VHR-10, to validate our approach. The results demonstrate that the proposed approach can effectively detect small targets in the VHR optical remote sensing images of about 10 × 10 pixels and automatically label small targets as well. In addition, our method presents better mean average precisions than other state-of-the-art methods: 1.5% higher when performing on the RSOD data set, 17.8% higher on the LEVIR data set, and 3.8% higher on NWPU VHR-10. Numéro de notice : A2019-595 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2921396 Date de publication en ligne : 15/07/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2921396 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94587
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 11 (November 2019) . - pp 8534 - 8545[article]Multi-sensor prediction of Eucalyptus stand volume: A support vector approach / Guilherme Silverio Aquino de Souza in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)
[article]
Titre : Multi-sensor prediction of Eucalyptus stand volume: A support vector approach Type de document : Article/Communication Auteurs : Guilherme Silverio Aquino de Souza, Auteur ; Vicente Paulo Soares, Auteur ; Helio Garcia Leite, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 135 - 146 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] bande L
[Termes IGN] Brésil
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal
[Termes IGN] Eucalyptus (genre)
[Termes IGN] image ALOS-AVNIR2
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] régression multiple
[Termes IGN] taux d'échantillonnage
[Termes IGN] volume en boisRésumé : (Auteur) Stem volume is a key attribute of Eucalyptus forest plantations upon which decision-making is based at diverse levels of planning. Quantifying volume through remote sensing can support a proper management of forests. Because of limitations on spaceborne optical and synthetic aperture radar sensors, this study integrated both types of datasets assembled using support vector regression (SVR) to retrieve the stand volume of Eucalyptus plantations. We assessed different combinations of sensors and a minimum number of plots to develop an SVR model. Finally, the best SVR performance was compared with other analytical methods already tested and in the literature: multilinear regression, artificial neural networks (ANN), and random forest (RF). Here, we introduce a test for comparative analysis of the performance of different methods. We found that SVR accurately predicted stem volume of Brazilian fast-growing Eucalyptus forest plantations. Gaussian radial basis was the most suitable kernel function. Integrating the optical and L-band backscatter data increased the predictive accuracy compared to a single sensor model. Combining NIR-band data from ALOS AVNIR-2 and backscatter of L-band horizontal emitted and vertical received (HV) electric fields from ALOS PALSAR produced the most accurate SVR model (with an R2 of 0.926 and root mean square error of 11.007 m3/ha). The number of field plots sufficient for model development with non-redundant explanatory variables was 77. Under this condition, SVR performed similarly to ANN and outperformed the multiple linear regression and random forest methods. Numéro de notice : A2019-319 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : doi.org/10.1016/j.isprsjprs.2019.08.002 Date de publication en ligne : 20/08/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93357
in ISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 135 - 146[article]Réservation
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