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Titre : Exploring the potential of deep learning for map generalization Type de document : Thèse/HDR Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Directeur de thèse ; Xiang Zhang, Directeur de thèse Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2023 Importance : 216 p. Note générale : bibliographie
Doctoral thesis from Université Gustave Eiffel, Doctoral school MSTIC, Specialty "Geographic information sciences"Langues : Anglais (eng) Descripteur : [Termes IGN] généralisation automatique de données
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] relation spatiale
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal profond
[Vedettes matières IGN] GénéralisationIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Map generalization is a process that aims to adapt the level of detail of geographic information for cartography at a small scale. Automating the process is complex but essential in map production. We think this research field could benefit from the recent advances in deep learning that make it possible to solve more and more complex tasks, using numerous training examples. This thesis proposes exploring the potential of deep learning for map generalization. This exploration is built upon three map generalization use cases: recognition of spatial relations, graphic generalization of mountain roads, and generalization of topographic maps at medium scales. These three use cases enable us to address research questions relative to the concrete implementation of deep learning models for map generalization (including dataset creation and architecture), the evaluation of such models and their integration in existing generalization processes. In addition to the models and training set adapted for each of our case studies already mentioned, we propose evaluation methods adapted to the challenges of cartographic generalization by deep learning. Finally, we propose a partitioning of the cartographic generalization into sub-problems facilitating the resolution by learning and allowing the generation of generalized map images. Note de contenu : Introduction
Part 1 A new paradigm for map generalization
Chapter A. Literature review
Chapter B. Formulating map generalization as a deep learning task
Chapter C. Designing a framework for deep learning based map generalization
Part 2 Exploration of deep learning for map generalization
Chapter D. Can graph neural networks model spatial relations?
Chapter E. CNN for the generalization of roads
Chapter F. The generation of topographic map with several themes
Part III The future of map generalization with deep learning
Chapter G. Usages of deep learning models for map generalization
Chapter H. Evaluation of deep learning predictions
ConclusionNuméro de notice : 17752 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Thèse française Organisme de stage : LASTIG (IGN) nature-HAL : Thèse DOI : sans Date de publication en ligne : 05/05/2023 En ligne : https://theses.hal.science/tel-04089883v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103186 GeoDanceHive: An operational hive for honeybees dances recording / Sylvain Galopin in Animals, vol 13 n° 7 (April-1 2023)
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Titre : GeoDanceHive: An operational hive for honeybees dances recording Type de document : Article/Communication Auteurs : Sylvain Galopin , Auteur ; Guillaume Touya , Auteur ; Pierrick Aupinel, Auteur ; Freddie-Jeanne Richard, Auteur Année de publication : 2023 Projets : 3-projet - voir note / Article en page(s) : n° 1182 Note générale : bibliographie
This research was funded by the french ministries of Agriculture and Food Sovereignty (MASA—FCPR program), Ecological Transition and Territorial Cohesion (MTECT), Health and Prevention (MSP) and Higher Education and Research (MESR) and by the French national facility for institutional procurement of VHR satellite imagery (DINAMIS) and by the Lune de Miel® Fondation. This research was financially supported by the French Office for Biodiversity, on the fee envelope for diffuse pollution of the Écophyto II+ coord plan. F-J Richard, partners P. Aupinel and G. Touya for the DANCE project.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] alimentation
[Termes IGN] comportement
[Termes IGN] enregistrement de données
[Termes IGN] Hymenoptera (ordre)Résumé : (auteur) Honeybees are known for their ability to communicate about resources in their environment. They inform the other foragers by performing specific dance sequences according to the spatial characteristics of the resource. The purpose of our study is to provide a new tool for honeybees dances recording, usable in the field, in a practical and fully automated way, without condemning the harvest of honey. We designed and equipped an outdoor prototype of a production hive, later called “GeoDanceHive”, allowing the continuous recording of honeybees’ behavior such as dances and their analysis. The GeoDanceHive is divided into two sections, one for the colony and the other serving as a recording studio. The time record of dances can be set up from minutes to several months. To validate the encoding and sampling quality, we used an artificial feeder and visual decoding to generate maps with the vector endpoints deduced from the dance information. The use of the GeoDanceHive is designed for a wide range of users, who can meet different objectives, such as researchers or professional beekeepers. Thus, our hive is a powerful tool for honeybees studies in the field and could highly contribute to facilitating new research approaches and a better understanding landscape ecology of key pollinators. Numéro de notice : A2023-087 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ani13071182 En ligne : https://doi.org/10.3390/ani13071182 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102987
in Animals > vol 13 n° 7 (April-1 2023) . - n° 1182[article]Geographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in Cartography and Geographic Information Science, vol inconnu (2023)
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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 localiséesRésumé : (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
in Cartography and Geographic Information Science > vol inconnu (2023)[article]GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates / Valerio Marsocci (2023)
Titre : GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates Type de document : Article/Communication Auteurs : Valerio Marsocci, Auteur ; Nicolas Gonthier, Auteur ; Anatol Garioud , Auteur ; Simone Scardapane, Auteur ; Clément Mallet , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2023 Conférence : CVPR 2023, IEEE Conference on Computer Vision and Pattern Recognition workshops 18/06/2023 22/06/2023 Vancouver Colombie britannique - Canada OA Proceedings Importance : 11 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] base de données d'occupation du sol
[Termes IGN] image à très haute résolution
[Termes IGN] jeu de données localisées
[Termes IGN] métadonnées géographiques
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, annotating large datasets to develop supervised systems for remote sensing (RS) semantic segmentation is costly and time-consuming. Unsupervised Domain Adaption (UDA) could tackle these issues by adapting a model trained on a source domain, where labels are available, to a target domain, without annotations. UDA, while gaining importance in computer vision, is still under-investigated in RS. Thus, we propose a new lightweight model, GeoMultiTaskNet, based on two contributions: a GeoMultiTask module (GeoMT), which utilizes geographical coordinates to align the source and target domains, and a Dynamic Class Sampling (DCS) strategy, to adapt the semantic segmentation loss to the frequency of classes. This approach is the first to use geographical metadata for UDA in semantic segmentation. It reaches state-of-the-art performances (47,22% mIoU), reducing at the same time the number of parameters (33M), on a subset of the FLAIR dataset, a recently proposed dataset properly shaped for RS UDA, used for the first time ever for research scopes here. Numéro de notice : C2023-004 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Communication DOI : 10.48550/arXiv.2304.07750 En ligne : https://doi.org/10.48550/arXiv.2304.07750 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103083 A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT / Shengli Tao in Earth System Science Data, vol 15 n° 4 (2023)
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Titre : A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT Type de document : Article/Communication Auteurs : Shengli Tao, Auteur ; Zurui Ao, Auteur ; Jean-Pierre Wigneron, Auteur ; Sassan Saatchi, Auteur ; Philippe Ciais, Auteur ; Jérôme Chave, Auteur ; Thuy Le Toan, Auteur ; Pierre-Louis Frison , Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 1577 - 1596 Note générale : bibliographie
Data description paperLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande C
[Termes IGN] bande Ku
[Termes IGN] fusion de données
[Termes IGN] image radar moirée
[Termes IGN] régression
[Termes IGN] série temporelleRésumé : (auteur) Satellite radar backscatter contains unique information on land surface moisture, vegetation features, and surface roughness and has thus been used in a range of Earth science disciplines. However, there is no single global radar data set that has a relatively long wavelength and a decades-long time span. We here provide the first long-term (since 1992), high-resolution (∼8.9 km instead of the commonly used ∼25 km resolution) monthly satellite radar backscatter data set over global land areas, called the long-term, high-resolution scatterometer (LHScat) data set, by fusing signals from the European Remote Sensing satellite (ERS; 1992–2001; C-band; 5.3 GHz), Quick Scatterometer (QSCAT, 1999–2009; Ku-band; 13.4 GHz), and the Advanced SCATterometer (ASCAT; since 2007; C-band; 5.255 GHz). The 6-year data gap between C-band ERS and ASCAT was filled by modelling a substitute C-band signal during 1999–2009 from Ku-band QSCAT signals and climatic information. To this end, we first rescaled the signals from different sensors, pixel by pixel. We then corrected the monthly signal differences between the C-band and the scaled Ku-band signals by modelling the signal differences from climatic variables (i.e. monthly precipitation, skin temperature, and snow depth) using decision tree regression. The quality of the merged radar signal was assessed by computing the Pearson r, root mean square error (RMSE), and relative RMSE (rRMSE) between the C-band and the corrected Ku-band signals in the overlapping years (1999–2001 and 2007–2009). We obtained high Pearson r values and low RMSE values at both the regional (r≥0.92, RMSE ≤ 0.11 dB, and rRMSE ≤ 0.38) and pixel levels (median r across pixels ≥ 0.64, median RMSE ≤ 0.34 dB, and median rRMSE ≤ 0.88), suggesting high accuracy for the data-merging procedure. The merged radar signals were then validated against the European Space Agency (ESA) ERS-2 data, which provide observations for a subset of global pixels until 2011, even after the failure of on-board gyroscopes in 2001. We found highly concordant monthly dynamics between the merged radar signals and the ESA ERS-2 signals, with regional Pearson r values ranging from 0.79 to 0.98. These results showed that our merged radar data have a consistent C-band signal dynamic. The LHScat data set (https://doi.org/10.6084/m9.figshare.20407857; Tao et al., 2023) is expected to advance our understanding of the long-term changes in, e.g., global vegetation and soil moisture with a high spatial resolution. The data set will be updated on a regular basis to include the latest images acquired by ASCAT and to include even higher spatial and temporal resolutions. Numéro de notice : A2023-097 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/essd-15-1577-2023 Date de publication en ligne : 12/04/2023 En ligne : https://doi.org/10.5194/essd-15-1577-2023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103215
in Earth System Science Data > vol 15 n° 4 (2023) . - pp 1577 - 1596[article]A hexagon-based method for polygon generalization using morphological operators / Lu Wang in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)PermalinkIncorporating ideas of structure and meaning in interactive multi scale mapping environments / Guillaume Touya in International journal of cartography, vol inconnu (2023)PermalinkIs the radial distance really a distance? An analysis of its properties and interest for the matching of polygon features / Yann Méneroux in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)PermalinkPermalinkMissing the city for buildings? A critical review of pan-scalar map generalization and design in contemporary zoomable maps / Maieul Gruget in International journal of cartography, vol inconnu (2023)PermalinkPermalinkModèles et outils pour la publication de métadonnées d'archives géographiques et de leurs données dérivées / Melvin Hersent (2023)PermalinkModern vectorization and alignment of historical maps: An application to Paris Atlas (1789-1950) / Yizi Chen (2023)PermalinkMulti-nomenclature, multi-resolution joint translation: an application to land-cover mapping / Luc Baudoux in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)PermalinkPermalink