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A hybrid data model for dynamic GIS: application to marine geomorphological dynamics / Younes Hamdani in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)
[article]
Titre : A hybrid data model for dynamic GIS: application to marine geomorphological dynamics Type de document : Article/Communication Auteurs : Younes Hamdani, Auteur ; Rémy Thibaud, Auteur ; Christophe Claramunt, Auteur Année de publication : 2021 Article en page(s) : pp 1475 - 1499 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] base de données spatiotemporelles
[Termes IGN] érosion
[Termes IGN] interface utilisateur
[Termes IGN] modèle conceptuel de données localisées
[Termes IGN] modèle dynamique
[Termes IGN] PostGIS
[Termes IGN] PostgreSQL
[Termes IGN] relief sous-marin
[Termes IGN] SIG dynamiqueRésumé : (auteur) The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model. Numéro de notice : A2021-547 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1829628 Date de publication en ligne : 12/10/2020 En ligne : https://doi.org/10.1080/13658816.2020.1829628 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98059
in International journal of geographical information science IJGIS > vol 35 n° 8 (August 2021) . - pp 1475 - 1499[article]Improving urban land cover classification with combined use of Sentinel-2 and Sentinel-1 imagery / Bin Hu in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)
[article]
Titre : Improving urban land cover classification with combined use of Sentinel-2 and Sentinel-1 imagery Type de document : Article/Communication Auteurs : Bin Hu, Auteur ; Yongyang Xu, Auteur ; Xiao Huang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 533 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Chine
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fusion de données
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] occupation du sol
[Termes IGN] planification urbaine
[Termes IGN] polarisation
[Termes IGN] zone urbaineRésumé : (auteur) Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery. Numéro de notice : A2021-590 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10080533 Date de publication en ligne : 09/08/2021 En ligne : https://doi.org/10.3390/ijgi10080533 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98210
in ISPRS International journal of geo-information > vol 10 n° 8 (August 2021) . - n° 533[article]Integrating GIS and location modeling: A relational approach / Ting L. Lei in Transactions in GIS, Vol 25 n° 4 (August 2021)
[article]
Titre : Integrating GIS and location modeling: A relational approach Type de document : Article/Communication Auteurs : Ting L. Lei, Auteur Année de publication : 2021 Article en page(s) : pp 1693 - 1715 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] allocation
[Termes IGN] base de données localisées
[Termes IGN] base de données relationnelles
[Termes IGN] langage de programmation
[Termes IGN] programmation linéaire
[Termes IGN] SQL
[Termes IGN] système de gestion de bases de données relationnellesRésumé : (auteur) Planners and geospatial analysts have long been using GIS and optimization methods to solve a wide range of spatial decision problems from routing and facility location to political redistricting. However, there has been a notable disconnection between the tools of GIS and optimization, constraining the capability of spatial decision-making in GIS. This article presents relational linear programming (RELP), a new framework and tool for bridging the gap between the two fields by integrating a generic optimization solver into GIS: a solver for integer linear programming (ILP). In particular, we choose relational/spatial databases as the GIS platform. We demonstrate that not only data, but also programs for spatial problems (in the form of ILP) can be managed and executed in SQL inside spatial databases. The main focus of this article is on location modeling, and we show how classic models can be formulated succinctly in a declarative way using SQL. The declarative approach of RELP allows researchers to focus on formulating new spatial decision problems without being bogged down in sophisticated programming. The integrated approach allows data processing and decision-making in one language. Therefore, it brings the power of location modeling to non-specialist GIS practitioners. Numéro de notice : A2021-700 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12804 Date de publication en ligne : 29/07/2021 En ligne : https://doi.org/10.1111/tgis.12804 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98575
in Transactions in GIS > Vol 25 n° 4 (August 2021) . - pp 1693 - 1715[article]Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America / Bin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)
[article]
Titre : Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America Type de document : Article/Communication Auteurs : Bin Chen, Auteur ; Ying Tu, Auteur ; Yimeng Song, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 203 - 218 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme d'apprentissage
[Termes IGN] carte d'utilisation du sol
[Termes IGN] données massives
[Termes IGN] données multisources
[Termes IGN] Etats-Unis
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] métropole
[Termes IGN] OpenStreetMap
[Termes IGN] planification urbaine
[Termes IGN] zone urbaineRésumé : (auteur) Urban land-use maps outlining the distribution, pattern, and composition of various land use types are critically important for urban planning, environmental management, disaster control, health protection, and biodiversity conservation. Recent advances in remote sensing and social sensing data and methods have shown great potentials in mapping urban land use categories, but they are still constrained by mixed land uses, limited predictors, non-localized models, and often relatively low accuracies. To inform these issues, we proposed a robust and cost-effective framework for mapping urban land use categories using openly available multi-source geospatial “big data”. With street blocks generated from OpenStreetMap (OSM) data as the minimum classification unit, we integrated an expansive set of multi-scale spatially explicit information on land surface, vertical height, socio-economic attributes, social media, demography, and topography. We further proposed to apply the automatic ensemble learning that leverages a bunch of machine learning algorithms in deriving optimal urban land use classification maps. Results of block-level urban land use classification in five metropolitan areas of the United States found the overall accuracies of major-class (Level-I) and minor-class (Level-II) classification could be high as 91% and 86%, respectively. A multi-model comparison revealed that for urban land use classification with high-dimensional features, the multi-layer stacking ensemble models achieved better performance than base models such as random forest, extremely randomized trees, LightGBM, CatBoost, and neural networks. We found without very-high-resolution National Agriculture Imagery Program imagery, the classification results derived from Sentinel-1, Sentinel-2, and other open big data based features could achieve plausible overall accuracies of Level-I and Level-II classification at 88% and 81%, respectively. We also found that model transferability depended highly on the heterogeneity in characteristics of different regions. The methods and findings in this study systematically elucidate the role of data sources, classification methods, and feature transferability in block-level land use classifications, which have important implications for mapping multi-scale essential urban land use categories. Numéro de notice : A2021-564 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.06.010 Date de publication en ligne : 25/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.06.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98129
in ISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 203 - 218[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021081 SL Revue Centre de documentation Revues en salle Disponible 081-2021083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Pattern-based identification and mapping of landscape types using multi-thematic data / Jakub Nowosad in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)
[article]
Titre : Pattern-based identification and mapping of landscape types using multi-thematic data Type de document : Article/Communication Auteurs : Jakub Nowosad, Auteur ; Tomasz F. Stepinski, Auteur Année de publication : 2021 Article en page(s) : pp 1634 - 1649 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] gestion des ressources
[Termes IGN] gestion foncière
[Termes IGN] matrice de co-occurrence
[Termes IGN] modèle mathématique
[Termes IGN] modélisation spatiale
[Termes IGN] occupation du sol
[Termes IGN] paysage
[Termes IGN] régionalisation (segmentation)
[Termes IGN] regroupement de données
[Termes IGN] segmentation en régionsRésumé : (auteur) Categorical maps of landscape types (LTs) are useful abstractions that simplify spatial and thematic complexity of natural landscapes, thus facilitating land resources management. A local landscape arises from a fusion of patterns of natural themes (such as land cover, landforms, etc.), which makes an unsupervised identification and mapping of LTs difficult. This paper introduces the integrated co-occurrence matrix (INCOMA) – a signature for numerical representation of multi-thematic categorical patterns. INCOMA enables an unsupervised identification and mapping of LTs. The region is tessellated into a large number of local landscapes – patterns of themes over small square-shaped neighborhoods. With local landscapes represented by INCOMA signatures and with dissimilarities between local landscapes calculated using the Jensen-Shannon Divergence (JSD), LTs can be identified and mapped using standard clustering or segmentation techniques. Resultant LTs are typically heterogeneous with respect to categories of contributing themes reflecting the human perception of a landscape. LTs calculated by INCOMA are more faithful abstractions of actual landscapes than LTs obtained by the current method of choice – the map overlay. The concept of INCOMA is described, and its application is demonstrated by an unsupervised mapping of LT zones in Europe based on combined patterns of land cover and landforms. Numéro de notice : A2021-549 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1893324 Date de publication en ligne : 02/03/2021 En ligne : https://doi.org/10.1080/13658816.2021.1893324 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98064
in International journal of geographical information science IJGIS > vol 35 n° 8 (August 2021) . - pp 1634 - 1649[article]Design and development 3D RRR model for Turkish cadastral system using international standards / Mehmet Alkan in Survey review, Vol 53 n° 379 (July 2021)PermalinkEvaluating the suitability of multi-scale terrain attribute calculation approaches for seabed mapping applications / Benjamin Misiuk in Marine geodesy, vol 44 n° 4 (July 2021)PermalinkA hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases / Chun Yang in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)PermalinkImplementing a mass valuation application on interoperable land valuation data model designed as an extension of the national GDI / Arif Cagdas Aydinoglu in Survey review, Vol 53 n° 379 (July 2021)PermalinkLayout graph model for semantic façade reconstruction using laser point clouds / Hongchao Fan in Geo-spatial Information Science, vol 24 n° 3 (July 2021)PermalinkReview of spectral indices for urban remote sensing / Akib Javed in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)PermalinkA scalable method to construct compact road networks from GPS trajectories / Yuejun Guo in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)PermalinkSpatio-temporal-spectral observation model for urban remote sensing / Zhenfeng Shao in Geo-spatial Information Science, vol 24 n° 3 (July 2021)PermalinkThe point-descriptor-precedence representation for point configurations and movements / Amna Qayyum in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)PermalinkThree-dimensional reconstruction of single input image based on point cloud / Yu Hou in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)Permalink