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The DORIS network: Advances achieved in the last fifteen years / Jérôme Saunier in Advances in space research, vol inconnu (2023)  

Public [article]  inAdvances in space research > vol inconnu (2023) Titre : The DORIS network: Advances achieved in the last fifteen years Type de document : Article/Communication Auteurs : Jérôme Saunier , Auteur Année de publication : 2023 Projets : 1-Pas de projet / Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] DORIS Résumé : (auteur) The Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) system is based on a homogeneous global geodetic network. The DORIS ground network is managed and monitored by a core group (CNES & IGN), which made it possible to closely steer its deployment and evolution. Thanks to infrastructure and hardware enhancements, the DORIS network has continuously improved over time in order to meet the performance requirements of satellite altimetry but also strengthen its role as geodetic network to contribute to the International Terrestrial Reference Frame (ITRF). Following the review by Fagard (2006) of the network from its initial deployment to its renovation, this paper aims at showing the advances achieved in the last fifteen years (2006–2021) to better serve the needs for precise orbit determination and geodesy. After reminding the historical background and the different stages of the network development, we zoom in the last decade that enabled the network to achieve improved operability thanks to infrastructure standardization, permanent monitoring and ongoing assessment. Today, the numerous strengths and assets of the DORIS network built up over 30 years give it an important role in contributing to Earth Sciences. This review shows the progress achieved in terms of geographical coverage, co-location with other techniques, data availability, stations equipment, monument stability, and system requirements compliance. Finally, we give an overview of the future prospects and new challenges to continue improvements in the DORIS technique. Numéro de notice : A2023-082 Affiliation des auteurs : IGN (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2022.07.016 En ligne : https://doi.org/10.1016/j.asr.2022.07.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101340 [article]

SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images / Hao Wu in Computers, Environment and Urban Systems, vol 100 (March 2023)  

Public [article]  inComputers, Environment and Urban Systems > vol 100 (March 2023) . - n° 101921 Titre : SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Wenting Luo, Auteur ; Anqi Lin, Auteur ; Fanghua Hao, Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Lanfa Liu, Auteur ; Yan Li, Auteur Année de publication : 2023 Projets : 1-Pas de projet / Article en page(s) : n° 101921 Note générale : Bibliographie This work was supported by the National Natural Science Foundation of China [42201468, 42071358], Postdoctoral Innovation Talents Support Program of China [BX20220128], China Postdoctoral Science Foundation [2022M721283] and Fundamental Research Funds for the Central Universities [CCNU22QN018]. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse multicritère [Termes IGN] apprentissage automatique [Termes IGN] boosting adapté [Termes IGN] cartographie urbaine [Termes IGN] Chine [Termes IGN] détection du bâti [Termes IGN] données localisées des bénévoles [Termes IGN] image à très haute résolution [Termes IGN] morphologie urbaine [Termes IGN] OpenStreetMap [Termes IGN] point d'intérêt [Termes IGN] représentation spatiale [Termes IGN] zone urbaine Résumé : (auteur) Urban functional zone mapping is essential for providing deeper insights into urban morphology and improving urban planning. The emergence of Volunteered Geographic Information (VGI), which provides abundant semantic data, offers a great opportunity to enrich land use information extracted from remote sensing (RS) images. Taking advantage of very-high-resolution (VHR) images and VGI data, this work proposed a SATL multifeature ensemble learning framework for mapping urban functional zones that integrated 65 features from the shapes of building objects, attributes of points of interest (POIs) tags, locations of cellphone users and textures of VHR images. The dimensionality of SALT features was reduced by the autoencoder, and the compressed features were applied to train the ensemble learning model composed of multiple classifiers for optimizing the urban functional zone classification. The effectiveness of the proposed framework was tested in an urbanized region of Nanchang City. The results indicated that the SALT features considering population dynamics and building shapes are comprehensive and feasible for urban functional zone mapping. The autoencoder has been proven efficient for dimension reduction of the original SALT features as it significantly improves the classification of urban functional zones. Moreover, the ensemble learning outperforms other machine learning models in terms of the accuracy and robustness when dealing with multi-classification tasks. Numéro de notice : A2023-125 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101921 Date de publication en ligne : 06/12/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101921 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102504 [article]

Geographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in Cartography and Geographic Information Science, vol inconnu (2023)  

Public [article]  inCartography and Geographic Information Science > vol inconnu (2023) Titre : Geographically masking addresses to study COVID-19 clusters Type de document : Article/Communication Auteurs : Walid Houfaf-Khoufaf, Auteur ; Guillaume Touya , Auteur Année de publication : 2023 Projets : 1-Pas de projet / Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] adresse postale [Termes IGN] anonymisation [Termes IGN] carte sanitaire [Termes IGN] classification barycentrique [Termes IGN] surveillance sanitaire [Termes IGN] traitement de données localisées Résumé : (auteur) The spatio-temporal analysis of cases is a good way an epidemic, and the recent COVID-19 pandemic unfortunately generated a huge amount of data. But analysing this raw data, with for instance the address of the people who contracted COVID-19, raises some privacy issues, and geomasking is necessary to preserve both people privacy and the spatial accuracy required for analysis. This paper proposes dierent geomasking techniques adapted to this COVID-19 data. Methods: Different techniques are adapted from the literature, and tested on a synthetic dataset mimicking the COVID-19 spatio-temporal spreading in Paris and a more rural nearby region. Theses techniques are assessed in terms of k-anonymity and cluster preservation. Results: Three adapted geomasking techniques are proposed: aggregation, bimodal gaussian perturbation, and simulated crowding. All three can be useful in different use cases, but the bimodal gaussian perturbation is the overall best techniques, and the simulated crowding is the most promising one, provided some improvements are introduced to avoid points with a low k-anonymity. Conclusions: It is possible to use geomasking techniques on addresses of people who caught COVID-19, while preserving the important spatial patterns. Numéro de notice : A2023-084 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers RSquare Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.1977709 Date de publication en ligne : 08/10/2021 En ligne : https://doi.org/10.1080/15230406.2021.1977709 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96857 [article]

Comparison of methods for the automatic classification of forest habitat types in the Southern Alps : Application to ecological data from the French national forest inventory / Charlotte Labit in Biodiversity & Conservation, vol 31 n° 13-14 (December 2022)  

Public [article]  inBiodiversity & Conservation > vol 31 n° 13-14 (December 2022) . - pp 3257 - 3283 Titre : Comparison of methods for the automatic classification of forest habitat types in the Southern Alps : Application to ecological data from the French national forest inventory Type de document : Article/Communication Auteurs : Charlotte Labit, Auteur ; Ingrid Bonhême , Auteur ; Sébastien Delhaye , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 3257 - 3283 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Alpes-de-haute-provence (04) [Termes IGN] Alpes-maritimes (06) [Termes IGN] analyse comparative [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] Drôme (26) [Termes IGN] habitat (nature) [Termes IGN] habitat forestier [Termes IGN] incertitude des données [Termes IGN] inventaire forestier national (données France) [Termes IGN] surveillance écologique [Vedettes matières IGN] Inventaire forestier Mots-clés libres : algorithm inspired by the habitat identification key used in the field Résumé : (auteur) The monitoring of habitats at plant association level, has been developed by the French-National Forest Inventory (NFI) progressively since 2011, whereas ecological and floristic data exist since the mid-1980s. The NFI habitat monitoring is the French tool of surveillance of forest habitats decreed by Natura 2000 Directive (article 11). Determination of plant association in NFI plots concerns all the habitats, whether they are of community interest or not. The objective of this study is to compare different methods of automatic classification of floristic and ecological surveys into forest habitat groups. Indeed, enriching the old surveys, which contain only ecological, floristic and trees data, with information on habitats would increase the accuracy of the calculated statistical results on habitats. The uncertainty of the attribution of a habitat outside the field (ex-situ) by experts was quantified by comparison with the determination in the field (in situ). This result was used as a benchmark to compare to the error rates obtained by two methods of automatic classification: an algorithm inspired by the habitat identification key used in the field and Random forest, a learning classification method. The classification performance was evaluated for three levels of habitat groupings. The results showed that the lower the level of clustering, the higher the error rate. Depending on the classification method used and the level of aggregation, the error rates varied between 5 and 15%. In all cases, the error rates were below the estimated uncertainty of the expert attribution of ex-situ habitat. Numéro de notice : A2022-696 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10531-022-02487-6 Date de publication en ligne : 25/10/2022 En ligne : https://doi.org/10.1007/s10531-022-02487-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101980 [article]

Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) / Vahid Nasiri in Arabian Journal of Geosciences, vol 15 n° 24 (December 2022)  

Public [article]  inArabian Journal of Geosciences > vol 15 n° 24 (December 2022) . - n° 1759 Titre : Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) Type de document : Article/Communication Auteurs : Vahid Nasiri, Auteur ; Arnaud Le Bris , Auteur ; Ali Asghar Darvishsefat, Auteur ; Fardin Moradi, Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 1759 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] aire protégée [Termes IGN] carte d'occupation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR Résumé : (auteur) Considering the importance of accurate and up-to-date land use/cover (LULC) maps and in a situation of fast LULC changes, an accurate mapping of complex landscapes requires real-time high-resolution remote sensed data and powerful classification algorithms. The new ESA Copernicus satellites Sentinel-1 (S-1) and Sentinel-2 (S-2) have contributed to the effective monitoring of the Earth’s surface. This paper aims at assessing the potential of mono-temporal S-1 and S-2 satellite images and three common classification algorithms including maximum likelihood (ML), support vector machine (SVM), and random forest (RF) for LULC classification. The research methodology consists of a sequence of tasks including data collection and preprocessing, the extraction of texture and spectral features, the definition of several feature set configurations, classification, and accuracy assessment. Based on the results, using S-1 data alone leads to quite poor results, even though dual polarimetric C-band and texture features increased the classification accuracy. The S-2 data outperformed the S-1 data in terms of overall and class level accuracies. A combined use of S-1 and S-2 satellite images involving extracted features from both sources led to the best result for identifying all classes. This emphasizes the critical importance of using multi-modal datasets and different features in the LULC classification. Among classification algorithms, the SVM led to the highest accuracies irrespective of the dataset. To sum it up, according to the applied methodology and results, S-1 and S-2 data can provide optimal and up-to-date information for LULC mapping using non-parametric classifiers as SVM or RF. Numéro de notice : A2022-699 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12517-022-11035-z Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1007/s12517-022-11035-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102253 [article]

ITRF2020 : un référentiel augmenté affinant la modélisation des mouvements non linéaires des stations / Zuheir Altamimi in XYZ, n° 173 (décembre 2022)

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Offering the appetite for the monitoring of European forests a diversified diet / Jean-Daniel Bontemps in Annals of Forest Science, vol 79 n° 1 (2022)  

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The role of wood harvest from sustainably managed forests in the carbon cycle / Ernst Detlef Schulze in Annals of Forest Science, vol 79 n° 1 (2022)  

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Location-enabled digital twins – understanding the role of NMCAs in a European context / Claire Ellul in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol X-4/W2 (October 2022)  

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Habitats, agricultural practices, and population dynamics of a threatened species: The European turtle dove in France / Christophe Sauser in Biological Conservation, vol 274 (octobre 2022)  

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Identifying the key resources and missing elements to build a knowledge graph dedicated to spatial dataset search / Mehdi Zrhal in Procedia Computer Science, vol 207 (2022)  

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Les temps des forêts et de leur observation / Jean-Daniel Bontemps in Revue forestière française, vol 73 n° 5 (2021)  

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Trade-offs between sustainable development goals in systems of cities / Juste Raimbault in Journal of Urban Management, vol 11 n° 2 (June 2022)  

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Analysis of massive imports of open data in Openstreetmap database: a study case for France / Arnaud Le Guilcher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)  

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Exploring digital twin adaptation to the urban environment: comparison with CIM to avoid silo-based approaches / Adeline Deprêtre in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)  

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