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From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 / Yousra Hamrouni in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 76 - 100 Titre : From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 Type de document : Article/Communication Auteurs : Yousra Hamrouni, Auteur ; Eric Paillassa, Auteur ; Véronique Chéret, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 76 - 100 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] base de données forestières [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] couvert forestier [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] France (administrative) [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] mise à jour de base de données [Termes descripteurs IGN] populus (genre) [Termes descripteurs IGN] série temporelle Résumé : (auteur) Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies because of data shift issues. In this paper, we propose the use of active learning to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing the classification performance. The adaptation consists in actively adding to the initial local model new relevant training samples from other areas in a cascade that iteratively improves the generalisation capabilities of the classifier leading to a global model tailored to these different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series revealed their high capacity to identify poplar plantations at a local scale with an average F-score ranging from 89.5% to 99.3%. For large area adaptation, the results showed that when the same number of training samples was used, active learning outperformed random sampling by up to 5% of the overall accuracy and up to 12% of the class F-score. Additionally, and depending on the class considered, the random sampling model required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for a national scale mapping in an operational context. Numéro de notice : A2021-013 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.018 date de publication en ligne : 20/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.018 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96417 [article]

A data fusion-based framework to integrate multi-source VGI in an authoritative land use database / Lanfa Liu in International Journal of Digital Earth, vol inconnu ([01/12/2020])  

Public [article]  inInternational Journal of Digital Earth > vol inconnu [01/12/2020] Titre : A data fusion-based framework to integrate multi-source VGI in an authoritative land use database Type de document : Article/Communication Auteurs : Lanfa Liu, Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Laurence Jolivet , Auteur ; Arnaud Le Bris , Auteur ; Linda M. See, Auteur Année de publication : 2020 Projets : 2-Pas d'info accessible - article non ouvert / Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] base de données localisées de référence [Termes descripteurs IGN] données hétérogènes [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] mise à jour de base de données [Termes descripteurs IGN] OCS GE [Termes descripteurs IGN] théorie de Dempster-Shafer Résumé : (auteur) Updating an authoritative Land Use and Land Cover (LULC) database requires many resources. Volunteered geographic information (VGI) involves citizens in the collection of data about their spatial environment. There is a growing interest in using existing VGI to update authoritative databases. This paper presents a framework aimed at integrating multi-source VGI based on a data fusion technique, in order to update an authoritative land use database. Each VGI data source is considered to be an independent source of information, which is fused together using Dempster-Shafer Theory (DST). The framework is tested in the updating of the authoritative land use data produced by the French National Mapping Agency. Four data sets were collected from several in-situ and remote campaigns run between 2018 and 2020 by contributors with varying profiles. The data fusion approach achieved an overall accuracy of 85.6% for the 144 features having at least two contributions when the confidence threshold was set to 0.05. Despite the heterogeneity and limited amount of VGI used, the results are promising, with 99% of the LU polygons updated or enriched. These results show the potential of using multi-source VGI to update or enrich authoritative LU data and potentially LULC data more generally. Numéro de notice : A2020-578 Affiliation des auteurs : LaSTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/17538947.2020.1842524 date de publication en ligne : 05/11/2020 En ligne : https://doi.org/10.1080/17538947.2020.1842524 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96522 [article]

Improving aboveground biomass estimates by taking into account density variations between tree components / Antoine Billard in Annals of Forest Science [en ligne], vol 77 n° 4 (December 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 77 n° 4 (December 2020) . - n° 103 Titre : Improving aboveground biomass estimates by taking into account density variations between tree components Type de document : Article/Communication Auteurs : Antoine Billard, Auteur ; Rodolphe Bauer, Auteur ; Frédéric Mothe, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 103 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] allométrie [Termes descripteurs IGN] base de données forestières [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] bois de chauffage [Termes descripteurs IGN] branche (arbre) [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] écorce [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] résineux [Termes descripteurs IGN] tomographie radar [Termes descripteurs IGN] volume en bois [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Key message: Strong density differences were observed between stem wood at 1.30 m and other tree components (stem wood, stem bark, knots, branch stumps and branches). The difference, up to 40% depending on the component, should be taken into account when estimating the biomass available for industrial uses, mainly fuelwood and wood for chemistry. Context: Basic density is a major variable in the calculation of tree biomass. However, it is usually measured on stem wood only and at breast height. Aims: The objectives of this study were to compare basic density of stem wood at 1.30 m with other tree components and assess the impact of differences on biomass. Methods: Three softwood species were studied: Abies alba Mill., Picea abies (L.) H. Karst., Pseudotsuga menziesii (Mirb.) Franco. X-Ray computed tomography was used to measure density. Results: Large differences were observed between components. Basic density of components was little influenced by tree size and stand density. Overall, bark, knot and branch biomasses were highly underestimated by using basic density measured at 1.30 m. Conclusion: Using available wood density databases mainly based on breast height measurements would lead to important biases (up to more than 40%) on biomass estimates for some tree components. Further work is necessary to complete available databases. Numéro de notice : A2020-714 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-020-00999-1 date de publication en ligne : 26/10/2020 En ligne : https://doi.org/10.1007/s13595-020-00999-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96282 [article]

Bretagne, la végétation cartographiée / Marielle Mayo in Géomètre, n° 2185 (novembre 2020)

Public [article]  inGéomètre > n° 2185 (novembre 2020) . - pp 46 - 49 Titre : Bretagne, la végétation cartographiée Type de document : Article/Communication Auteurs : Marielle Mayo, Auteur Année de publication : 2020 Article en page(s) : pp 46 - 49 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes descripteurs IGN] 1:25.000 [Termes descripteurs IGN] acquisition d'images [Termes descripteurs IGN] aménagement régional [Termes descripteurs IGN] appariement semi-automatique [Termes descripteurs IGN] ArcGIS [Termes descripteurs IGN] BD ortho [Termes descripteurs IGN] Bretagne [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] données publiques [Termes descripteurs IGN] écologie végétale [Termes descripteurs IGN] IGN cité [Termes descripteurs IGN] image infrarouge couleur [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] modèle orienté objet Résumé : (Auteur) Une cartographie inédite de la végétation de Bretagne sera accessible en totalité en ligne en décembre. Produite par télédétection grâce à une méthode semi-automatisée innovante, elle répond aux nouveaux besoins des acteurs de la biodiversité et de l'aménagement du territoire. Numéro de notice : A2020-707 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96281 [article]

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Delivering time-evolving 3D city models for web visualization / Vincent Jaillot in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 10 (October 2020) . - pp 2030 - 2052 Titre : Delivering time-evolving 3D city models for web visualization Type de document : Article/Communication Auteurs : Vincent Jaillot, Auteur ; Sylvie Servigne, Auteur ; Gilles Gesquière, Auteur Année de publication : 2020 Article en page(s) : pp 2030 - 2052 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] CityGML [Termes descripteurs IGN] implémentation (informatique) [Termes descripteurs IGN] interactivité [Termes descripteurs IGN] modèle 3D de l'espace urbain [Termes descripteurs IGN] modèle conceptuel de données [Termes descripteurs IGN] modélisation spatio-temporelle [Termes descripteurs IGN] Open geospatial consortium Résumé : (auteur) Studying and planning urban evolution is essential for understanding the past and designing the cities of the future and can be facilitated by providing means for sharing, visualizing, and navigating in cities, on the web, in space and in time. Standard formats, methods, and tools exist for visualizing large-scale 3D cities on the web. In this paper, we go further by integrating the temporal dimension of cities in geospatial web delivery standard formats. In doing so, we enable interactive visualization of large-scale time-evolving 3D city models on the web. A key characteristic of this paper lies in the proposed four-step generic approach. First, we design a generic conceptual model of standard formats for delivering 3D cities on the web. Then, we formalize and integrate the temporal dimension of cities into this generic conceptual model. Following which, we specify the conceptual model in the 3D Tiles standard at logical and technical specification levels, resulting in an extension of 3D Tiles for delivering time-evolving 3D city models on the web. Finally, we propose an open-source implementation, experiments, and an evaluation of the propositions and visualization rules. We also provide access to reproducibility notes allowing researchers to replicate all the experiments. Numéro de notice : A2020-514 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1749637 date de publication en ligne : 14/04/2020 En ligne : https://doi.org/10.1080/13658816.2020.1749637 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95673 [article]

A semantic graph database for the interoperability of 3D GIS data / Eva Savina Malinverni in Applied geomatics, vol 12 n° 3 (September 2020)  

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Integration of spatialization and individualization: the future of epidemic modelling for communicable diseases / Meifang Li in Annals of GIS, vol 26 n° 3 (July 2020)  

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A hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery / Mehdi Khoshboresh Masouleh in Applied geomatics, vol 12 n° 2 (June 2020)  

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Mining spatiotemporal association patterns from complex geographic phenomena / Zhanjun He in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

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Unsupervised change detection between SAR images based on hypergraphs / Jun Wang in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

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Methodology of the automatic generalization of buildings, road networks, forests and surface waters: a case study based on the Topographic Objects Database in Poland / Izabela Karsznia in Geocarto international, vol 35 n° 7 ([15/05/2020])  

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Mapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics / E. Banzhaf in Geocarto international, vol 35 n° 6 ([01/05/2020])  

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Study of usability of aerial images and high-resolution satellite images in cadastre renewal works in Turkey / Fazil Nacar in Survey review, vol 52 n° 372 (May 2020)  

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Classifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks / Lauwrence V. Stanislawski in International journal of cartography, Vol 6 n° 1 (March 2020)  

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Classification of time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne (2020)  

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