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Provenance in GIServices: A semantic web approach / Zhaoyan Wu in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)  

Public [article]  inISPRS International journal of geo-information > vol 12 n° 3 (March 2023) . - n° 118 Titre : Provenance in GIServices: A semantic web approach Type de document : Article/Communication Auteurs : Zhaoyan Wu, Auteur ; Hao Li, Auteur ; Peng Yue, Auteur Année de publication : 2023 Article en page(s) : n° 118 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] données localisées [Termes IGN] métadonnées [Termes IGN] ontologie [Termes IGN] OWL [Termes IGN] service web [Termes IGN] service web sémantique [Termes IGN] source de données [Termes IGN] système d'information géographique [Termes IGN] web sémantique Résumé : (auteur) Recent developments in Web Service and Semantic Web technologies have shown great promise for the automatic chaining of geographic information services (GIService), which can derive user-specific information and knowledge from large volumes of data in the distributed information infrastructure. In order for users to have an informed understanding of products generated automatically by distributed GIServices, provenance information must be provided to them. This paper describes a three-level conceptual view of provenance: the automatic capture of provenance in the semantic execution engine; the query and inference of provenance. The view adapts well to the three-phase procedure for automatic GIService composition and can increase understanding of the derivation history of geospatial data products. Provenance capture in the semantic execution engine fits well with the Semantic Web environment. Geospatial metadata is tracked during execution to augment provenance. A prototype system is implemented to illustrate the applicability of the approach. Numéro de notice : A2023-157 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi12030118 En ligne : https://doi.org/10.3390/ijgi12030118 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102848 [article]

Dendrometric data from the silvicultural scenarios developed by Office National des Forêts (ONF) in France: a tool for applied research and carbon storage estimates / Salomé Fournier in Annals of Forest Science, vol 79 n° 1 (2022)  

Public [article]  inAnnals of Forest Science > vol 79 n° 1 (2022) . - n° 48 Titre : Dendrometric data from the silvicultural scenarios developed by Office National des Forêts (ONF) in France: a tool for applied research and carbon storage estimates Type de document : Article/Communication Auteurs : Salomé Fournier, Auteur ; Thierry Sardin, Auteur ; Philippe Dreyfus, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] dendrométrie [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] écosystème forestier [Termes IGN] forêt publique [Termes IGN] France (administrative) [Termes IGN] gestion forestière [Termes IGN] métadonnées [Termes IGN] puits de carbone [Vedettes matières IGN] Sylviculture Résumé : (auteur) Key message: We provide a database of 52 silvicultural scenarios recommended in French public forests including relevant dendrometric variables and metrics for carbon accounting. The dataset is available at https://doi.org/10.57745/QARRFS. Associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/f76ed27f-325d-493b-8731-0995dcaa7805. Special attention was paid to offer carbon metrics required for the French Label Bas Carbone offset projects. Background: Forests are a source of multiple ecosystem services that benefit society (e.g. wood production, global climate regulation, habitat for biodiversity, landscape amenities, etc.; Brockerhoff et al. 2017), most of which are influenced by forest management. In this regard, European forests are characterised by their wide variety of forest types (Barbati et al. 2007), therefore maintaining the important range of associated forest ecosystem services in a changing climate might require to explore a range of alternative forest management and assess their impact to adapt European forests (Mina et al. 2017; Biber et al. 2020)[...] . Numéro de notice : A2022-904 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s13595-022-01171-7 Date de publication en ligne : 28/12/2022 En ligne : https://doi.org/10.1186/s13595-022-01171-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102315 [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]

Fully automated pose estimation of historical images in the context of 4D geographic information systems utilizing machine learning methods / Ferdinand Maiwald in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 11 (November 2021) . - n° 748 Titre : Fully automated pose estimation of historical images in the context of 4D geographic information systems utilizing machine learning methods Type de document : Article/Communication Auteurs : Ferdinand Maiwald, Auteur ; Christoph Lehmann, Auteur ; Taras Lazariv, Auteur Année de publication : 2021 Article en page(s) : n° 748 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] apprentissage automatique [Termes IGN] chaîne de traitement [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] corrélation à l'aide de traits caractéristiques [Termes IGN] échelle de temps [Termes IGN] estimation de pose [Termes IGN] image ancienne [Termes IGN] image terrestre [Termes IGN] métadonnées [Termes IGN] modélisation 4D [Termes IGN] patrimoine culturel [Termes IGN] recherche d'image basée sur le contenu [Termes IGN] récupération de données [Termes IGN] structure-from-motion [Termes IGN] système d'information géographique Résumé : (auteur) The idea of virtual time machines in digital environments like hand-held virtual reality or four-dimensional (4D) geographic information systems requires an accurate positioning and orientation of urban historical images. The browsing of large repositories to retrieve historical images and their subsequent precise pose estimation is still a manual and time-consuming process in the field of Cultural Heritage. This contribution presents an end-to-end pipeline from finding relevant images with utilization of content-based image retrieval to photogrammetric pose estimation of large historical terrestrial image datasets. Image retrieval as well as pose estimation are challenging tasks and are subjects of current research. Thereby, research has a strong focus on contemporary images but the methods are not considered for a use on historical image material. The first part of the pipeline comprises the precise selection of many relevant historical images based on a few example images (so called query images) by using content-based image retrieval. Therefore, two different retrieval approaches based on convolutional neural networks (CNN) are tested, evaluated, and compared with conventional metadata search in repositories. Results show that image retrieval approaches outperform the metadata search and are a valuable strategy for finding images of interest. The second part of the pipeline uses techniques of photogrammetry to derive the camera position and orientation of the historical images identified by the image retrieval. Multiple feature matching methods are used on four different datasets, the scene is reconstructed in the Structure-from-Motion software COLMAP, and all experiments are evaluated on a newly generated historical benchmark dataset. A large number of oriented images, as well as low error measures for most of the datasets, show that the workflow can be successfully applied. Finally, the combination of a CNN-based image retrieval and the feature matching methods SuperGlue and DISK show very promising results to realize a fully automated workflow. Such an automated workflow of selection and pose estimation of historical terrestrial images enables the creation of large-scale 4D models. Numéro de notice : A2021-827 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10110748 Date de publication en ligne : 04/11/2021 En ligne : https://doi.org/10.3390/ijgi10110748 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98964 [article]

Integration of heterogeneous terrain data into Discrete Global Grid Systems / Mingke Li in Cartography and Geographic Information Science, vol 48 n° 6 (October 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 6 (October 2021) . - pp 546 - 564 Titre : Integration of heterogeneous terrain data into Discrete Global Grid Systems Type de document : Article/Communication Auteurs : Mingke Li, Auteur ; Heather McGrath, Auteur ; Emmanuel Stefanakis, Auteur Année de publication : 2021 Article en page(s) : pp 546 - 564 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] données de terrain [Termes IGN] données hétérogènes [Termes IGN] Infrastructure de données [Termes IGN] métadonnées [Termes IGN] modèle numérique de surface [Termes IGN] Ontario (Canada) [Termes IGN] système de grille globale discrète [Termes IGN] tessellation Résumé : (auteur) The Canadian Digital Elevation Model (CDEM) and the High-Resolution Digital Elevation Model (HRDEM) released by Natural Resources Canada are primary terrain data sources in Canada. Due to their different coverage, datums, resolutions, and accuracies, a standardized framework for national elevation data across various scales is required. This study provides new insights into the adoption of Discrete Global Grid Systems (DGGS) to facilitate the integration of multi-source terrain data at various granularities. In particular, the Icosahedral Snyder Equal Area Aperture 3 Hexagonal Grid (ISEA3H) was employed, and quantization, integration, and aggregation were conducted on this framework. To demonstrate the modeling process, an experiment was undertaken for two areas in Ontario, taking advantage of parallel computing which was beneficial from the discreteness of DGGS cells. The accuracy of the modeled elevations was estimated by referring to the ground-surveyed values and was included in the spatially referenced metadata as an indicator of data quality. This research can serve as a guide for future development of a national elevation service, providing consistent, multi-resolution elevations and avoiding complex, duplicated pre-processing at the user’s end. Future investigation into an operational integration platform to support real-world decision-making, as well as the DGGS-powered geospatial datacube, is recommended. Numéro de notice : A2021-748 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1966648 Date de publication en ligne : 02/09/2021 En ligne : https://doi.org/10.1080/15230406.2021.1966648 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98711 [article]

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