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DiffusionNet: discretization agnostic learning on surfaces / Nicholas Sharp in ACM Transactions on Graphics, TOG, Vol 41 n° 3 (June 2022)  

Public [article]  inACM Transactions on Graphics, TOG > Vol 41 n° 3 (June 2022) . - pp 1 - 16 Titre : DiffusionNet: discretization agnostic learning on surfaces Type de document : Article/Communication Auteurs : Nicholas Sharp, Auteur ; Souhaib Attaiki, Auteur ; K. Crane, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage profond [Termes IGN] discrétisation [Termes IGN] maillage [Termes IGN] Perceptron multicouche [Termes IGN] Triangular Regular Network [Termes IGN] voisinage (relation topologique) Résumé : (auteur) We introduce a new general-purpose approach to deep learning on three-dimensional surfaces based on the insight that a simple diffusion layer is highly effective for spatial communication. The resulting networks are automatically robust to changes in resolution and sampling of a surface—a basic property that is crucial for practical applications. Our networks can be discretized on various geometric representations, such as triangle meshes or point clouds, and can even be trained on one representation and then applied to another. We optimize the spatial support of diffusion as a continuous network parameter ranging from purely local to totally global, removing the burden of manually choosing neighborhood sizes. The only other ingredients in the method are a multi-layer perceptron applied independently at each point and spatial gradient features to support directional filters. The resulting networks are simple, robust, and efficient. Here, we focus primarily on triangle mesh surfaces and demonstrate state-of-the-art results for a variety of tasks, including surface classification, segmentation, and non-rigid correspondence. Numéro de notice : A2022-321 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1145/3507905 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1145/3507905 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100369 [article]

Self-organizing maps as a dimension reduction approach for spatial global sensitivity analysis visualization / Seda Şalap-Ayça in Transactions in GIS, vol 26 n° 4 (June 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 4 (June 2022) . - pp 1718 - 1734 Titre : Self-organizing maps as a dimension reduction approach for spatial global sensitivity analysis visualization Type de document : Article/Communication Auteurs : Seda Şalap-Ayça, Auteur Année de publication : 2022 Article en page(s) : pp 1718 - 1734 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie [Termes IGN] analyse de groupement [Termes IGN] analyse de sensibilité [Termes IGN] carte de Kohonen [Termes IGN] représentation spatiale [Termes IGN] réseau neuronal artificiel [Termes IGN] visualisation cartographique [Termes IGN] voisinage (relation topologique) Résumé : (auteur) Spatial global sensitivity analysis (SGSA) reveals and ranks the input–output relation in spatial models. The SGSA output is twofold: (1) first-order effects which are the linear relations of every input layer with the output; and (2) high-order effects where the nonlinear interaction among input layers is depicted. The resulting sensitivity maps are twice the number of input layers which is challenging to visualize, considering the limitations of the human cognitive system or visual representations. Finding similar patterns and projecting that similarity into a 2D surface will help to tackle this voluminous visual load. This article presents the implementation of self-organizing maps (SOM), a type of artificial neural network, as a dimension reduction approach for SGSA visualization. SOM is also used for feature selection to identify the most relevant feature for model uncertainty. The winning neurons at SOM are projected as the influence map and the results are compared with conventional visualization techniques. Numéro de notice : A2022-532 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12963 Date de publication en ligne : 21/06/2022 En ligne : https://doi.org/10.1111/tgis.12963 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101080 [article]

Multi-resolution representation using graph database / Yizhi Huang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2022 (2022 edition) . - pp 173 - 180 Titre : Multi-resolution representation using graph database Type de document : Article/Communication Auteurs : Yizhi Huang, Auteur ; Emmanuel Stefanakis, Auteur Année de publication : 2022 Article en page(s) : pp 173 - 180 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] base de données de graphes [Termes IGN] interface utilisateur [Termes IGN] objet géographique [Termes IGN] représentation multiple [Termes IGN] requête spatiale Mots-clés libres : Neo4j Résumé : (auteur) Multi-resolution representation has always been an important and popular data source for many research and applications, such as navigation, land cover, map generation, media event forecasting, etc. With one spatial object represented by distinct geometries at different resolutions, multi-resolution representation is high in complexity. Most of the current approaches for storing and retrieving multi-resolution representation are either complicated in structure, or time consuming in traversal and query. In addition, supports on direct navigation between different representations are still intricate in most of the paradigms, especially in topological map sets. To address this problem, we propose a novel approach for storing, querying, and extracting multi-resolution representation. The development of this approach is based on Neo4j, a graph database platform that is famous for its powerful query and advanced flexibility. Benefited from the intuitiveness of the proposed database structure, direct navigation between representations of one spatial object, and between groups of representations at adjacent resolutions are both available. On top of this, collaborating with the self-designed web-based interface, queries within the proposed approach truly embraced the concept of keyword search, which lower the barrier between novice users and complicate queries. In all, the proposed system demonstrates the potential of managing multi-resolution representation data through the graph database and could be a time-saver for related processes. Numéro de notice : A2022-425 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-4-2022-173-2022 Date de publication en ligne : 18/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2022-173-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100730 [article]

Discovering co-location patterns in multivariate spatial flow data / Jiannan Cai 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 720 - 748 Titre : Discovering co-location patterns in multivariate spatial flow data Type de document : Article/Communication Auteurs : Jiannan Cai, Auteur ; Mei-Po Kwan, Auteur Année de publication : 2022 Article en page(s) : pp 720 - 748 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse bivariée [Termes IGN] analyse de groupement [Termes IGN] analyse univariée [Termes IGN] autocorrélation spatiale [Termes IGN] Chicago (Illinois) [Termes IGN] co-positionnement [Termes IGN] données de flux [Termes IGN] données socio-économiques [Termes IGN] dynamique spatiale [Termes IGN] enquête [Termes IGN] exploration de données géographiques [Termes IGN] migration pendulaire [Termes IGN] origine - destination [Termes IGN] voisinage (relation topologique) Résumé : (auteur) Spatial flow co-location patterns (FCLPs) are important for understanding the spatial dynamics and associations of movements. However, conventional point-based co-location pattern discovery methods ignore spatial movements between locations and thus may generate erroneous findings when applied to spatial flows. Despite recent advances, there is still a lack of methods for analyzing multivariate flows. To bridge the gap, this paper formulates a novel problem of FCLP discovery and presents an effective detection method based on frequent-pattern mining and spatial statistics. We first define a flow co-location index to quantify the co-location frequency of different features in flow neighborhoods, and then employ a bottom-up method to discover all frequent FCLPs. To further establish the statistical significance of the results, we develop a flow pattern reconstruction method to model the benchmark null hypothesis of independence conditioning on univariate flow characteristics (e.g. flow autocorrelation). Synthetic experiments with predefined FCLPs verify the advantages of our method in terms of correctness over available alternatives. A case study using individual home-work commuting flow data in the Chicago Metropolitan Area demonstrates that residence- or workplace-based co-location patterns tend to overestimate the co-location frequency of people with different occupations and could lead to inconsistent results. Numéro de notice : A2022-256 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1980217 Date de publication en ligne : 20/09/2021 En ligne : https://doi.org/10.1080/13658816.2021.1980217 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100229 [article]

Spatially oriented convolutional neural network for spatial relation extraction from natural language texts / Qinjun Qiu in Transactions in GIS, vol 26 n° 2 (April 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 2 (April 2022) . - pp 839 - 866 Titre : Spatially oriented convolutional neural network for spatial relation extraction from natural language texts Type de document : Article/Communication Auteurs : Qinjun Qiu, Auteur ; Zhong Xie, Auteur ; Kai Ma, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 839 - 866 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] appariement sémantique [Termes IGN] apprentissage dirigé [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] exploration de données [Termes IGN] langage naturel (informatique) [Termes IGN] proximité sémantique [Termes IGN] relation spatiale [Termes IGN] relation topologique [Termes IGN] site wiki [Termes IGN] spatial metrics [Termes IGN] système à base de connaissances Résumé : (auteur) Spatial relation extraction (e.g., topological relations, directional relations, and distance relations) from natural language descriptions is a fundamental but challenging task in several practical applications. Current state-of-the-art methods rely on rule-based metrics, either those specifically developed for extracting spatial relations or those integrated in methods that combine multiple metrics. However, these methods all rely on developed rules and do not effectively capture the characteristics of natural language spatial relations because the descriptions may be heterogeneous and vague and may be context sparse. In this article, we present a spatially oriented piecewise convolutional neural network (SP-CNN) that is specifically designed with these linguistic issues in mind. Our method extends a general piecewise convolutional neural network with a set of improvements designed to tackle the task of spatial relation extraction. We also propose an automated workflow for generating training datasets by integrating new sentences with those in a knowledge base, based on string similarity and semantic similarity, and then transforming the sentences into training data. We exploit a spatially oriented channel that uses prior human knowledge to automatically match words and understand the linguistic clues to spatial relations, finally leading to an extraction decision. We present both the qualitative and quantitative performance of the proposed methodology using a large dataset collected from Wikipedia. The experimental results demonstrate that the SP-CNN, with its supervised machine learning, can significantly outperform current state-of-the-art methods on constructed datasets. Numéro de notice : A2022-365 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12887 Date de publication en ligne : 27/12/2021 En ligne : https://doi.org/10.1111/tgis.12887 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100584 [article]

Road network generalization method constrained by residential areas / Zheng Lyu in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)  

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Raw GIS to 3D road modeling for real-time traffic simulation / Yacine Amara in The Visual Computer, vol 38 n° 1 (January 2022)  

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CrossroadsDescriber, automatic textual description of OpenStreetMap intersections / Jérémy Kalsron (2022)  

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Incorporation of spatial anisotropy in urban expansion modelling with cellular automata / Jinqu Zhang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)  

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Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation / Hang Zhang in Pattern recognition, vol 121 (January 2022)  

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Semi-automatic reconstruction of object lines using a smartphone’s dual camera / Mohammed Aldelgawy in Photogrammetric record, Vol 36 n° 176 (December 2021)  

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A topology-based graph data model for indoor spatial-social networking / Mahdi Rahimi in International journal of geographical information science IJGIS, vol 35 n° 12 (December 2021)  

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Linear regression and lines intersecting as a method of extracting punctual entities in a lidar point cloud / Marlo Antonio Ribeiro Martins in Boletim de Ciências Geodésicas, vol 27 n° 3 ([01/10/2021])  

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Loosening the grid: topology as the basis for a more inclusive GIS / L. Westerveld in International journal of geographical information science IJGIS, vol 35 n° 10 (October 2021)  

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Indoor space as the basis for modelling of buildings in a 3D Cadastre / Jernej Tekavec in Survey review, Vol 53 n° 380 (September 2021)  

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