Centre de documentation
scientifique

IGN : Cartes sur portable / Jean-Pierre Maillard in XYZ, n° 179 (juin 2024)

Public [article]  inXYZ > n° 179 (juin 2024) . - pp. 16 Titre : IGN : Cartes sur portable Type de document : Article/Communication Auteurs : Jean-Pierre Maillard, Auteur Année de publication : 2024 Article en page(s) : pp. 16 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Vedettes matières IGN] Géomatique [Termes IGN] carte [Termes IGN] carte en 3D [Termes IGN] navigation pédestre [Termes IGN] téléphonie mobile Résumé : (Auteur) Le 15 mai 2024, en l’hôtel de Roquelaure, Christophe Béchu, ministre de la Transition écologique et de la Cohésion des territoires a officiellement lancé l’application “Cartes IGN”, un outil de smartphone qui trouvera grandement preneur par sa gratuité et son nul besoin de créer de compte. Présentée par Sébastien Soriano, directeur général de l’IGN, "Cartes IGN" est une création “maison”, au service de tous, avec l’objectif d’une mise à jour en continu. Sa valeur ajoutée est considérable, puisque les autres applications actuellement existantes renseignent peu sur l’environnement et ne fournissent que la représentation de 10 % du territoire, seulement objet d’une actualisation cyclique. Comme les autres, “Cartes IGN” permet de se déplacer, mais aussi, novation oblige, d’explorer le territoire avec un grand niveau de détails et de mesurer son évolution avec un retour possible sur le passé. Enfin, l’actualisation constante du plan constituant un fondement du projet, une fonction “Signaler” donne aux utilisateurs l’opportunité d’y contribuer en demandant toute modification qui apparaît utile. Numéro de notice : A2024-17901 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtSansCL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103657 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
112-2024021	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

Improvement in crop mapping from satellite image time series by effectively supervising deep neural networks / Sina Mohammadi in ISPRS Journal of photogrammetry and remote sensing, vol 198 (April 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 198 (April 2023) . - pp 272 - 283 Titre : Improvement in crop mapping from satellite image time series by effectively supervising deep neural networks Type de document : Article/Communication Auteurs : Sina Mohammadi, Auteur ; Mariana Belgiu, Auteur ; Alfred Stein, Auteur Année de publication : 2023 Article en page(s) : pp 272 - 283 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage dirigé [Termes IGN] apprentissage profond [Termes IGN] carte de la végétation [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] cultures [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] série temporelle Résumé : (auteur) Deep learning methods have achieved promising results in crop mapping using satellite image time series. A challenge still remains on how to better learn discriminative feature representations to detect crop types when the model is applied to unseen data. To address this challenge and reveal the importance of proper supervision of deep neural networks in improving performance, we propose to supervise intermediate layers of a designed 3D Fully Convolutional Neural Network (FCN) by employing two middle supervision methods: Cross-entropy loss Middle Supervision (CE-MidS) and a novel middle supervision method, namely Supervised Contrastive loss Middle Supervision (SupCon-MidS). This method pulls together features belonging to the same class in embedding space, while pushing apart features from different classes. We demonstrate that SupCon-MidS enhances feature discrimination and clustering throughout the network, thereby improving the network performance. In addition, we employ two output supervision methods, namely F1 loss and Intersection Over Union (IOU) loss. Our experiments on identifying corn, soybean, and the class Other from Landsat image time series in the U.S. corn belt show that the best set-up of our method, namely IOU+SupCon-MidS, is able to outperform the state-of-the-art methods by scores of 3.5% and 0.5% on average when testing its accuracy across a different year (local test) and different regions (spatial test), respectively. Further, adding SupCon-MidS to the output supervision methods improves scores by 1.2% and 7.6% on average in local and spatial tests, respectively. We conclude that proper supervision of deep neural networks plays a significant role in improving crop mapping performance. The code and data are available at: https://github.com/Sina-Mohammadi/CropSupervision. Numéro de notice : A2023-203 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2023.03.007 Date de publication en ligne : 29/03/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.03.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103105 [article]

An experiment on the role of participatory GIS in the adjudication process of customary lands / Kwabena Asiama in Survey review, vol 55 n° 389 (March 2023)  

Public [article]  inSurvey review > vol 55 n° 389 (March 2023) . - pp 178 - 191 Titre : An experiment on the role of participatory GIS in the adjudication process of customary lands Type de document : Article/Communication Auteurs : Kwabena Asiama, Auteur ; Anthony Arko-Adjei, Auteur Année de publication : 2023 Article en page(s) : pp 178 - 191 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes IGN] carte photographique [Termes IGN] carte thématique [Termes IGN] droit coutumier [Termes IGN] droit foncier [Termes IGN] Ghana [Termes IGN] SIG participatif [Termes IGN] système d'information foncière [Termes IGN] utilisation du sol Résumé : (auteur) This study presents the results from an experiment conducted in two peri-urban areas of Northern Ghana using Participatory GIS (PGIS) to identify land tenure and use rights on customary and statutory lands. P-Mapping was used to uncover indigenous knowledge on the changes in land ownership, land use rights and land-use types over ten years. The paper finds that properly trained local people can reliably delineate and indicate land rights and land uses in their environment on photomaps with little support from professionals. The experiment results show that PGIS can accelerate land adjudication processes on customary lands. Numéro de notice : A2023-136 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2022.2040869 Date de publication en ligne : 23/03/2022 En ligne : https://doi.org/10.1080/00396265.2022.2040869 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102687 [article]

Deriving map images of generalised mountain roads with generative adversarial networks / Azelle Courtial in International journal of geographical information science IJGIS, vol 37 n° 3 (March 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 3 (March 2023) . - pp 499 - 528 Titre : Deriving map images of generalised mountain roads with generative adversarial networks Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Année de publication : 2023 Article en page(s) : pp 499 - 528 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative [Termes IGN] apprentissage dirigé [Termes IGN] apprentissage non-dirigé [Termes IGN] carte routière [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] généralisation cartographique automatisée [Termes IGN] montagne [Termes IGN] réseau antagoniste génératif [Vedettes matières IGN] Généralisation Résumé : (auteur) Map generalisation is a process that transforms geographic information for a cartographic at a specific scale. The goal is to produce legible and informative maps even at small scales from a detailed dataset. The potential of deep learning to help in this task is still unknown. This article examines the use case of mountain road generalisation, to explore the potential of a specific deep learning approach: generative adversarial networks (GAN). Our goal is to generate images that depict road maps generalised at the 1:250k scale, from images that depict road maps of the same area using un-generalised 1:25k data. This paper not only shows the potential of deep learning to generate generalised mountain roads, but also analyses how the process of deep learning generalisation works, compares supervised and unsupervised learning and explores possible improvements. With this experiment we have exhibited an unsupervised model that is able to generate generalised maps evaluated as good as the reference and reviewed some possible improvements for deep learning-based generalisation, including training set management and the definition of a new road connectivity loss. All our results are evaluated visually using a four questions process and validated by a user test conducted on 113 individuals. Numéro de notice : A2023-073 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2123488 Date de publication en ligne : 20/10/2022 En ligne : https://doi.org/10.1080/13658816.2022.2123488 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101901 [article]

Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence / Sidi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 197 (March 2023) . - pp 199 - 211 Titre : Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence Type de document : Article/Communication Auteurs : Sidi Wu, Auteur ; Konrad Schindler, Auteur ; Magnus Heitzler, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 199 - 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte ancienne [Termes IGN] cartographie historique [Termes IGN] classification dirigée [Termes IGN] détection de changement [Termes IGN] données anciennes [Termes IGN] matrice de co-occurrence [Termes IGN] réseau antagoniste génératif [Termes IGN] segmentation d'image [Termes IGN] vision par ordinateur Résumé : (auteur) Historical maps depict past states of the Earth’s surface and make it possible to trace the natural or anthropogenic evolution of geographic objects back through time. However, the state of the depicted reality is not the only source of change: maps of varying age can differ in terms of graphical design, and also in terms of storage conditions, physical ageing of pigments, and the scanning process for digitization. Consequently, a computer vision system learned from a specific (source) map series will often not generalize well to older or newer (target) maps, calling for domain adaptation. In the present paper we examine – to our knowledge for the first time – domain adaptation for segmenting historical maps. We argue that for geo-spatial data like maps, which are geo-localized by definition, the spatial co-occurrence of geographical objects provides a supervision signal for domain adaptation. Since only a subset of all mapped objects co-occur, and even those are not perfectly aligned due to both real topographic changes and variations in map generalization/production, they only provide weak supervision — still they can bring a substantial benefit over completely unsupervised domain adaptation methods. The core of our proposed method is a novel self-supervised co-occurrence network that detects co-occurring objects across maps (specifically, domains) with a novel loss function that allows for object changes and spatial misalignment. Experiments show that, for the task of segmenting hydrological objects such as rivers, lakes and wetlands, our system significantly outperforms two state-of-art baselines, even with limited supervision (e.g., 5%). The source code is publicly available at https://github.com/sian-wusidi/spatialcooccurrence. Numéro de notice : A2023-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2023.01.021 Date de publication en ligne : 14/02/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.01.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102804 [article]

Mapping population distribution from open address data: application to mainland Portugal / Nelson Mileu in Journal of maps, vol 18 n° 3 (March 2023)  

Permalink

Des mesures au sol aux images satellite : quelles données pour étudier la pollution lumineuse ? / Christophe Plotard in XYZ, n° 174 (mars 2023)

Permalink

The potential of combining satellite and airborne remote sensing data for habitat classification and monitoring in forest landscapes / Anna Iglseder in International journal of applied Earth observation and geoinformation, vol 117 (March 2023)  

Permalink

Who owns the map? Data sovereignty and government spatial data collection, use, and dissemination / Peter A. Johnson in Transactions in GIS, vol 27 n° 1 (February 2023)  

Permalink

A GIS-based method for modeling methane emissions from paddy fields by fusing multiple sources of data / Linhua Ma in Science of the total environment, vol 859 n° 1 (February 2023)  

Permalink

Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models / Xikun Hu in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)  

Permalink

Multi-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)  

Permalink

Cartographier l'anthropocène 2023 / IGN (2023)    

Permalink

Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data / Thuong V. Tran in GIScience and remote sensing, vol 60 n° 1 (2023)  

Permalink

Entry separation using a mixed visual and textual language model: Application to 19th century French trade directories / Bertrand Duménieu (2023)  

Permalink