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scientifique

Outliers and uncertainties in GNSS ZTD estimates from double-difference processing and precise point positioning / Katarzyna Stępniak in GPS solutions, vol 26 n° 3 (July 2022)  

Public [article]  inGPS solutions > vol 26 n° 3 (July 2022) . - n° 74 Titre : Outliers and uncertainties in GNSS ZTD estimates from double-difference processing and precise point positioning Type de document : Article/Communication Auteurs : Katarzyna Stępniak, Auteur ; Olivier Bock , Auteur ; Pierre Bosser , Auteur ; Pawel Wielgosz, Auteur Année de publication : 2022 Projets : VEGAN / Bock, Olivier Article en page(s) : n° 74 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données GNSS [Termes IGN] double différence [Termes IGN] ERA5 [Termes IGN] incertitude des données [Termes IGN] positionnement ponctuel précis [Termes IGN] retard troposphérique zénithal [Termes IGN] valeur aberrante [Vedettes matières IGN] Traitement de données GNSS Résumé : (auteur) Double-difference (DD) analysis and precise point positioning (PPP) are two widely used processing approaches to analyze ground-based GNSS measurements. We investigate the quality of the zenith tropospheric delay (ZTD) estimates produced from both processing approaches for a regional network over 1 year and show that DD solutions contain more numerous and larger ZTD outliers. The accuracy of both DD and PPP solutions strongly depends on the data processing procedure and models. We analyze the impact of mapping functions, satellite orbit and clock products and ambiguity resolution (fixed vs. float) on ZTD estimates. The results are assessed from station position repeatability and ZTD differences with respect to the ERA5 reanalysis. As expected, mapping functions have the strongest impact, with VMF1 being more accurate than GMF. Surprisingly, the impact of the ambiguity resolution and satellite products is rather weak in the PPP solution. We speculate that this results from the fact that final satellite products have reached a high level of accuracy and that other error sources now dominate static PPP solutions. A time and frequency analysis reveal unprecedented spurious sub-daily signals in the ZTD time series, which occur at the frequency of the GPS satellite repeat period and its harmonics. This suggests that sub-daily GPS ZTD estimates contain a significant part of the residual modeling errors due to satellite orbits, tidal models, mapping functions and multipath, which still need to be improved. Numéro de notice : A2022-359 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-022-01261-z Date de publication en ligne : 29/04/2022 En ligne : https://doi.org/10.1007/s10291-022-01261-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100578 [article]

Constraint-based evaluation of map images generalized by deep learning / Azelle Courtial in Journal of Geovisualization and Spatial Analysis, vol 6 n° 1 (June 2022)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 6 n° 1 (June 2022) . - n° 13 Titre : Constraint-based evaluation of map images generalized by deep learning Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Bock, Olivier Article en page(s) : n° 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] connexité (graphes) [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] montagne [Termes IGN] programmation par contraintes [Termes IGN] qualité des données [Termes IGN] rendu réaliste [Termes IGN] route [Vedettes matières IGN] Généralisation Résumé : (auteur) Deep learning techniques have recently been experimented for map generalization. Although promising, these experiments raise new problems regarding the evaluation of the output images. Traditional map generalization evaluation cannot directly be applied to the results in a raster format. Additionally, the internal evaluation used by deep learning models is mostly based on the realism of images and the accuracy of pixels, and none of these criteria is sufficient to evaluate a generalization process. Finally, deep learning processes tend to hide the causal mechanisms and do not always guarantee a result that follows cartographic principles. In this article, we propose a method to adapt constraint-based evaluation to the images generated by deep learning models. We focus on the use case of mountain road generalization, and detail seven raster-based constraints, namely, clutter, coalescence reduction, smoothness, position preservation, road connectivity preservation, noise absence, and color realism constraints. These constraints can contribute to current studies on deep learning-based map generalization, as they can help guide the learning process, compare different models, validate these models, and identify remaining problems in the output images. They can also be used to assess the quality of training examples. Numéro de notice : A2022-332 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41651-022-00104-2 Date de publication en ligne : 07/05/2022 En ligne : http://dx.doi.org/10.1007/s41651-022-00104-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100646 [article]

GIS-KG: building a large-scale hierarchical knowledge graph for geographic information science / Jiaxin Du in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 5 (May 2022) . - pp 873 - 897 Titre : GIS-KG: building a large-scale hierarchical knowledge graph for geographic information science Type de document : Article/Communication Auteurs : Jiaxin Du, Auteur ; Shaohua Wang, Auteur ; Xinyue Ye, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 873 - 897 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage profond [Termes IGN] approche hiérarchique [Termes IGN] exploration de données [Termes IGN] ingénierie des connaissances [Termes IGN] ontologie [Termes IGN] recherche d'information géographique [Termes IGN] réseau sémantique [Termes IGN] traitement du langage naturel Résumé : (auteur) An organized knowledge base can facilitate the exploration of existing knowledge and the detection of emerging topics in a domain. Knowledge about and around Geographic Information Science and its associated system technologies (GIS) is complex, extensive and emerging rapidly. Taking the challenge, we built a GIS knowledge graph (GIS-KG) by (1) merging existing GIS bodies of knowledge to create a hierarchical ontology and then (2) applying deep-learning methods to map GIS publications to the ontology. We conducted several experiments on information retrieval to evaluate the novelty and effectiveness of the GIS-KG. Results showed the robust support of GIS-KG for knowledge search of existing GIS topics and potential to explore emerging research themes. Numéro de notice : A2022-341 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2005795 Date de publication en ligne : 26/11/2021 En ligne : https://doi.org/10.1080/13658816.2021.2005795 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100515 [article]

Accuracy issues for spatial update of digital cadastral maps / David Pullar in ISPRS International journal of geo-information, vol 11 n° 4 (April 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 4 (April 2022) . - n° 221 Titre : Accuracy issues for spatial update of digital cadastral maps Type de document : Article/Communication Auteurs : David Pullar, Auteur ; Stephen Donaldson, Auteur Année de publication : 2022 Article en page(s) : n° 221 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cadastre étranger [Termes IGN] Australie [Termes IGN] base de données foncières [Termes IGN] compensation [Termes IGN] données cadastrales [Termes IGN] lever cadastral [Termes IGN] méthode des moindres carrés [Termes IGN] mise à jour [Termes IGN] parcelle cadastrale [Termes IGN] plan parcellaire [Termes IGN] précision des données Résumé : (auteur) All geospatial data are updated periodically. Cadastral parcel mapping, however, has special update requirements that set it apart from other geospatial data. Mapped boundaries change continuously to fit with new survey plans. Additionally, new parcels have to be fitted and aligned with adjoining parcels to merge them into existing cadastral mapping. This is preferably performed by a spatial adjustment approach to systematically improve its accuracy over time. This paper adapts methods for analysis and adjustment of survey networks to improve the accuracy of cadastral mapping with better coordinate positioning and survey plan dimensions. Case studies for both hypothetical and real cadastral mapping are used to illustrate the issues and spatially resolve errors. Adjustment results achieve an accuracy consistent with other GIS layers and boundary features visible in high-resolution orthoimagery. Graphical charts based on stress–strain relationships provide a simplified means to interpret post-adjustment results to identify and fix potential errors. Numéro de notice : A2022-285 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/ijgi11040221 Date de publication en ligne : 24/03/2022 En ligne : https://doi.org/10.3390/ijgi11040221 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100305 [article]

Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation / Yingjie Hu in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 799 - 821 Titre : Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation Type de document : Article/Communication Auteurs : Yingjie Hu, Auteur ; Zhipeng Gui, Auteur ; Jimin Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 799 - 821 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] descripteur [Termes IGN] données d'entrainement sans étiquette [Termes IGN] image cartographique [Termes IGN] métadonnées [Termes IGN] projection [Termes IGN] système d'information géographique [Termes IGN] Web Map Service [Termes IGN] web mapping Résumé : (auteur) Maps in the form of digital images are widely available in geoportals, Web pages, and other data sources. The metadata of map images, such as spatial extents and place names, are critical for their indexing and searching. However, many map images have either mismatched metadata or no metadata at all. Recent developments in deep learning offer new possibilities for enriching the metadata of map images via image-based information extraction. One major challenge of using deep learning models is that they often require large amounts of training data that have to be manually labeled. To address this challenge, this paper presents a deep learning approach with GIS-based data augmentation that can automatically generate labeled training map images from shapefiles using GIS operations. We utilize such an approach to enrich the metadata of map images by adding spatial extents and place names extracted from map images. We evaluate this GIS-based data augmentation approach by using it to train multiple deep learning models and testing them on two different datasets: a Web Map Service image dataset at the continental scale and an online map image dataset at the state scale. We then discuss the advantages and limitations of the proposed approach. Numéro de notice : A2022-258 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/13658816.2021.1968407 En ligne : https://doi.org/10.1080/13658816.2021.1968407 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100231 [article]

Identification and classification of routine locations using anonymized mobile communication data / Gonçalo Ferreira in ISPRS International journal of geo-information, vol 11 n° 4 (April 2022)  

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Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction / Jincai Huang in Transactions in GIS, vol 26 n° 2 (April 2022)  

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The integration of multi-source remotely sensed data with hierarchically based classification approaches in support of the classification of wetlands / Aaron Judah in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)  

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Accessing spatial knowledge networks with maps / Markus Jobst in International journal of cartography, vol 8 n° 1 (March 2022)  

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Automated 3D reconstruction of LoD2 and LoD1 models for All 10 million buildings of the Netherlands / Ravi Peters in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)  

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Évaluation des apports de l’apprentissage profond au sein d’un service dédié à la numérisation du patrimoine / Maxime Mérizette in XYZ, n° 170 (mars 2022)

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Mise à jour du registre de l’EPSG suite aux évolutions du RGF93 / Thierry Gattacceca in XYZ, n° 170 (mars 2022)

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Analysis of factors affecting adoption of volunteered geographic information in the context of national spatial data infrastructure / Munir Ahmad in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)  

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Architecture for semantic web service composition in spatial data infrastructures / Deniztan Ulutaş Karakol in Survey review, vol 54 n° 382 (January 2022)  

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Attention-based vandalism detection in OpenStreetMap / Nicolas Tempelmeier (2022)  

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