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Using OpenStreetMap data and machine learning to generate socio-economic indicators / Daniel Feldmeyer in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)
[article]
Titre : Using OpenStreetMap data and machine learning to generate socio-economic indicators Type de document : Article/Communication Auteurs : Daniel Feldmeyer, Auteur ; Claude Meisch, Auteur ; Holger Sauter, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Allemagne
[Termes IGN] apprentissage automatique
[Termes IGN] arbre aléatoire
[Termes IGN] base de données spatiotemporelles
[Termes IGN] changement climatique
[Termes IGN] chômage
[Termes IGN] classification par réseau neuronal
[Termes IGN] collectivité territoriale
[Termes IGN] données localisées des bénévoles
[Termes IGN] données socio-économiques
[Termes IGN] inégalité
[Termes IGN] limite administrative
[Termes IGN] modèle de régression
[Termes IGN] modèle de simulation
[Termes IGN] OpenStreetMapRésumé : (auteur) Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-economic indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-economic indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or economic inequalities. Numéro de notice : A2020-663 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9090498 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.3390/ijgi9090498 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96139
in ISPRS International journal of geo-information > vol 9 n° 9 (September 2020) . - 16 p.[article]Structure from motion for complex image sets / Mario Michelini in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
[article]
Titre : Structure from motion for complex image sets Type de document : Article/Communication Auteurs : Mario Michelini, Auteur ; Helmut Mayer, Auteur Année de publication : 2020 Article en page(s) : pp 140 - 152 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] arbre aléatoire minimum
[Termes IGN] caméra numérique
[Termes IGN] distorsion d'image
[Termes IGN] étalonnage d'instrument
[Termes IGN] fusion de données multisource
[Termes IGN] itération
[Termes IGN] jeu de données
[Termes IGN] orientation
[Termes IGN] reconstruction 3D
[Termes IGN] SIFT (algorithme)
[Termes IGN] structure-from-motionRésumé : (auteur) This paper presents an approach for Structure from Motion (SfM) for unorganized complex image sets. To achieve high accuracy and robustness, image triplets are employed and an (approximate) internal camera calibration is assumed to be known. The complexity of an image set is determined by the camera configurations which may include wide as well as weak baselines. Wide baselines occur for instance when terrestrial images and images from small Unmanned Aerial Systems (UAS) are combined. The resulting large (geometric/radiometric) distortions between images make image matching difficult possibly leading to an incomplete result. Weak baselines mean an insufficient distance between cameras compared to the distance of the observed scene and give rise to critical camera configurations. Inappropriate handling of such configurations may lead to various problems in triangulation-based SfM up to total failure. The focus of our approach lies on a complete linking of images even in case of wide or weak baselines. We do not rely on any additional information such as camera configurations, Global Positioning System (GPS) or an Inertial Navigation System (INS). As basis for generating suitable triplets to link the images, an iterative graph-based method is employed formulating image linking as the search for a terminal Steiner minimum tree in the line graph. SIFT (Lowe, 2004) descriptors are embedded into Hamming space for fast image similarity ranking. This is employed to limit the number of pairs to be geometrically verified by a computationally and more complex wide baseline matching method (Mayer et al., 2012). Critical camera configurations which are not suitable for geometric verification are detected by means of classification (Michelini and Mayer, 2019). Additionally, we propose a graph-based approach for the optimization of the hierarchical merging of triplets to efficiently generate larger image subsets. By this means, a complete, 3D reconstruction of the scene is obtained. Experiments demonstrate that the approach is able to produce reliable orientation for large image sets comprising wide as well as weak baseline configurations. Numéro de notice : A2020-355 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.020 Date de publication en ligne : 12/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.020 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95242
in ISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 140 - 152[article]Réservation
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Titre : Planar maps, random walks and circle packing : École d'été de probabilités de Saint-Flour XLVIII - 2018 Type de document : Guide/Manuel Auteurs : Asaf Nachmias, Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Collection : Lecture notes in Mathematics num. 2243 Importance : 120 p. ISBN/ISSN/EAN : 978-3-030-27968-4 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] arbre aléatoire
[Termes IGN] fonction harmonique
[Termes IGN] graphe planaire
[Termes IGN] modèle de MarkovIndex. décimale : 23.60 Statistiques et probabilités Résumé : (Editeur) This open access book focuses on the interplay between random walks on planar maps and Koebe’s circle packing theorem. Further topics covered include electric networks, the He–Schramm theorem on infinite circle packings, uniform spanning trees of planar maps, local limits of finite planar maps and the almost sure recurrence of simple random walks on these limits. One of its main goals is to present a self-contained proof that the uniform infinite planar triangulation (UIPT) is almost surely recurrent. Full proofs of all statements are provided. A planar map is a graph that can be drawn in the plane without crossing edges, together with a specification of the cyclic ordering of the edges incident to each vertex. One widely applicable method of drawing planar graphs is given by Koebe’s circle packing theorem (1936). Various geometric properties of these drawings, such as existence of accumulation points and bounds on the radii, encode important probabilistic information, such as the recurrence/transience of simple random walks and connectivity of the uniform spanning forest. This deep connection is especially fruitful to the study of random planar maps. The book is aimed at researchers and graduate students in mathematics and is suitable for a single-semester course; only a basic knowledge of graduate level probability theory is assumed. Note de contenu : 1. Introduction
1.1 The Circle Packing Theorem
1.2 Probabilistic Applications
2. Random Walks and Electric Networks
2.1 Harmonic Functions and Voltages
2.2 Flows and Currents
2.3 The Effective Resistance of a Network
2.4 Energy
2.5 Infinite Graphs
2.6 Random Paths
2.7 Exercises
3. The CirclePacking Theorem
3.1 Planar Graphs, Maps and Embeddings
3.2 Proof of the Circle Packing Theorem
4. Parabolic and Hyperbolic Packings
4.1 Infinite Planar Maps
4.2 The Ring Lemma and Infinite Circle Packings
4.3 Statement of the He–Schramm Theorem
4.4 Proof of the He–Schramm Theorem
4.5 Exercises
5. Planar Local Graph Limits
5.1 Local Convergenceof Graphs and Maps
5.2 The Magic Lemma
5.3 Recurrence of Bounded Degree Planar Graph Limits
5.4 Exercises
6. Recurrence of Random Planar Maps
6.1 Star-Tree Transform
6.2 Stationary Random Graphs and Markings
6.3 Proof of Theorem
7. Uniform Spanning Trees of Planar Graphs
7.1 Introduction
7.2 Basic Properties of the UST
7.3 Limits over Exhaustions:The Free and Wired USF
7.4 Planar Duality
7.5 Connectivity of the Free Forest
7.6 Exercises
8. Related TopicsNuméro de notice : 26541 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Manuel de cours DOI : 10.1007/978-3-030-27968-4 En ligne : http://doi.org/10.1007/978-3-030-27968-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97764 Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation / Li Mi in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)
[article]
Titre : Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation Type de document : Article/Communication Auteurs : Li Mi, Auteur ; Zhenzhong Chen, Auteur Année de publication : 2020 Article en page(s) : pp 140 - 152 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme SLIC
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image à très haute résolution
[Termes IGN] processus stochastique
[Termes IGN] réseau neuronal profond
[Termes IGN] segmentation sémantique
[Termes IGN] superpixelRésumé : (Auteur) Semantic segmentation plays an important role in remote sensing image understanding. Great progress has been made in this area with the development of Deep Convolutional Neural Networks (DCNNs). However, due to the complexity of ground objects’ spectrum, DCNNs with simple classifier have difficulties in distinguishing ground object categories even though they can represent image features effectively. Additionally, DCNN-based semantic segmentation methods learn to accumulate contextual information over large receptive fields that causes blur on object boundaries. In this work, a novel approach named Superpixel-enhanced Deep Neural Forest (SDNF) is proposed to target the aforementioned problems. To improve the classification ability, we introduce Deep Neural Forest (DNF), where the representation learning of deep neural network is conducted by a completely differentiable decision forest. Therefore, better classification accuracy is achieved by combining DCNNs with decision forests in an end-to-end manner. In addition, considering the homogeneity within superpixels and heterogeneity between superpixels, a Superpixel-enhanced Region Module (SRM) is proposed to further alleviate the noises and strengthen edges of ground objects. Experimental results on the ISPRS 2D semantic labeling benchmark demonstrate that our model significantly outperforms state-of-the-art methods thus validate the efficiency of our proposed SDNF. Numéro de notice : A2020-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.006 Date de publication en ligne : 29/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.006 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94403
in ISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 140 - 152[article]Réservation
Réserver ce documentExemplaires (3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020011 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020013 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020012 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani in Annals of forest research, vol 62 n° 1 (January - June 2019)
[article]
Titre : Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest Type de document : Article/Communication Auteurs : Aline Bernarda Debastiani, Auteur ; Carlos Roberto Sanquetta, Auteur ; Ana Paula Dalla Corte, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 109 - 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Amazonie
[Termes IGN] apprentissage automatique
[Termes IGN] arbre aléatoire
[Termes IGN] bande C
[Termes IGN] biomasse aérienne
[Termes IGN] Brésil
[Termes IGN] forêt tropicale
[Termes IGN] fusion d'images
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] réseau neuronal convolutifRésumé : (auteur) The aim of the present study is to evaluate the potential of C-band SAR data from the Sentinel-1/2 instruments and machine learning algorithms for the estimation of forest above ground forest biomass (AGB) in a high-biomass tropical ecosystem. This study was carried out in Jamari National Forest, located in the Brazilian Amazon. The response variable was AGB (Mg/ha) estimated from airborne laser surveys. The following treatments were considered as model predictors: 1) Sentinel-1 Sigma 0 at VV and VH polarizations; 2) (1) plus Sentinel-1 textural metrics; 3) (2) plus Sentinel-2 bands and derived vegetation indices (LAI, RVI, SAVI, NDVI).Our modeling design estimated the relative importance of SAR vs. optical variables in explaining AGB. The modeling was performed with twelve machine-learning algorithms including, neural network and regression tree. The addition of texture and optical data provided a noticeable improvement (3%) over models with SAR backscatter only. The best model performance was achieved with the Random Tree algorithm. Our results demonstrate the potential of freely-available SAR data and machine learning for mapping AGB in tropical ecosystems. Numéro de notice : A2019-335 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.15287/afr.2018.1267 Date de publication en ligne : 30/07/2019 En ligne : http://dx.doi.org/10.15287%2Fafr.2018.1267 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93349
in Annals of forest research > vol 62 n° 1 (January - June 2019) . - pp 109 - 122[article]Improving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco / Hassane Rahali in Geocarto international, vol 34 n° 1 ([01/01/2019])PermalinkPermalinkDetection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach / Josselin Aval in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkOn the spatial distribution of buildings for map generalization / Zhiwei Wei in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)PermalinkAutomated extraction of 3D vector topographic feature line from terrain point cloud / Wei Zhou in Geocarto international, vol 33 n° 10 (October 2018)PermalinkA deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)PermalinkGeometric reasoning with uncertain polygonal faces / Jochen Meidow in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)PermalinkSeed dispersal, microsites or competition : what drives gap regeneration in an old-growth forest? An application of spatial point process modelling / Georg Gratzer in Forests, vol 9 n° 5 (May 2018)PermalinkEvaluation of 10-year temporal and spatial variability in structure and growth across contrasting commercial thinning treatments in spruce-fir forests of northern Maine, USA / Christian Kuehne in Annals of Forest Science, vol 75 n° 1 (March 2018)PermalinkPermalink