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Computing and querying strict, approximate, and metrically refined topological relations in linked geographic data / Blake Regalia in Transactions in GIS, vol 23 n° 3 (June 2019)
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Titre : Computing and querying strict, approximate, and metrically refined topological relations in linked geographic data Type de document : Article/Communication Auteurs : Blake Regalia, Auteur ; Krzysztof Janowicz, Auteur ; Grant McKenzie, Auteur Année de publication : 2019 Article en page(s) : pp 601 - 619 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] DBpedia
[Termes IGN] données localisées
[Termes IGN] entité géographique
[Termes IGN] graphe
[Termes IGN] relation topologique
[Termes IGN] requête spatiale
[Termes IGN] réseau sémantique
[Termes IGN] web des donnéesRésumé : (Auteur) Geographic entities and the information associated with them play a major role in Web‐scale knowledge graphs such as Linked Data. Interestingly, almost all major datasets represent places and even entire regions as point coordinates. There are two key reasons for this. First, complex geometries are difficult to store and query using the current Linked Data technology stack to a degree where many queries take minutes to return or will simply time out. Second, the absence of complex geometries confirms a common suspicion among GIScientists, namely that for many everyday queries place‐based relational knowledge is more relevant than raw geometries alone. To give an illustrative example, the statement that the White House is in Washington, DC is more important for gaining an understating of the city than the exact geometries of both entities. This does not imply that complex geometries are unimportant but that (topological) relations should also be extracted from them. As Egenhofer and Mark (1995b) put it in their landmark paper on naive geography, topology matters, metric refines. In this work we demonstrate how to compute and utilize strict, approximate, and metrically refined topological relations between several geographic feature types in DBpedia and compare our results to approaches that compute result sets for topological queries on the fly. Numéro de notice : A2019-256 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12548 Date de publication en ligne : 26/06/2019 En ligne : https://doi.org/10.1111/tgis.12548 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93014
in Transactions in GIS > vol 23 n° 3 (June 2019) . - pp 601 - 619[article]Deeply integrating linked data with geographic information systems / Gengchen Mai in Transactions in GIS, vol 23 n° 3 (June 2019)
[article]
Titre : Deeply integrating linked data with geographic information systems Type de document : Article/Communication Auteurs : Gengchen Mai, Auteur ; Krzysztof Janowicz, Auteur ; Bo Yan, Auteur ; Simon Scheider, Auteur Année de publication : 2019 Article en page(s) : pp 579 - 600 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] connecteur logiciel
[Termes IGN] graphe
[Termes IGN] ontologie
[Termes IGN] réseau sémantique
[Termes IGN] système d'information géographique
[Termes IGN] web des donnéesRésumé : (Auteur) The realization that knowledge often forms a densely interconnected graph has fueled the development of graph databases, Web‐scale knowledge graphs and query languages for them, novel visualization and query paradigms, as well as new machine learning methods tailored to graphs as data structures. One such example is the densely connected and global Linked Data cloud that contains billions of statements about numerous domains, including life science and geography. While Linked Data has found its way into everyday applications such as search engines and question answering systems, there is a growing disconnect between the classical ways in which Geographic Information Systems (GIS) are still used today and the open‐ended, exploratory approaches used to retrieve and consume data from knowledge graphs such as Linked Data. In this work, we conceptualize and prototypically implement a Linked Data connector framework as a set of toolboxes for Esri's ArcGIS to close this gap and enable the retrieval, integration, and analysis of Linked Data from within GIS. We discuss how to connect to Linked Data endpoints, how to use ontologies to probe data and derive appropriate GIS representations on the fly, how to make use of reasoning, how to derive data that are ready for spatial analysis out of RDF triples, and, most importantly, how to utilize the link structure of Linked Data to enable analysis. The proposed Linked Data connector framework can also be regarded as the first step toward a guided geographic question answering system over geographic knowledge graphs. Numéro de notice : A2019-255 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12538 Date de publication en ligne : 11/06/2019 En ligne : https://doi.org/10.1111/tgis.12538 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93013
in Transactions in GIS > vol 23 n° 3 (June 2019) . - pp 579 - 600[article]Piecewise-planar approximation of large 3D data as graph-structured optimization / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W5 (May 2019)
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Titre : Piecewise-planar approximation of large 3D data as graph-structured optimization Type de document : Article/Communication Auteurs : Stéphane Guinard , Auteur ; Loïc Landrieu , Auteur ; Laurent Caraffa , Auteur ; Bruno Vallet , Auteur Année de publication : 2019 Projets : 1-Pas de projet / Conférence : ISPRS 2019, Geospatial Week 10/06/2019 14/06/2019 Enschede Pays-Bas ISPRS OA Annals Article en page(s) : pp 365 - 372 Note générale : bibliographie
The authors would like to acknowledge the DGA for their financial support of this work.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme de décalage moyen
[Termes IGN] analyse de groupement
[Termes IGN] approximation
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] érosion anthropique
[Termes IGN] erreur d'approximation
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] graphe
[Termes IGN] maillage
[Termes IGN] Ransac (algorithme)
[Termes IGN] semis de points
[Termes IGN] surface planeRésumé : (auteur) We introduce a new method for the piecewise-planar approximation of 3D data, including point clouds and meshes. Our method is designed to operate on large datasets (e.g. millions of vertices) containing planar structures, which are very frequent in anthropic scenes. Our approach is also adaptive to the local geometric complexity of the input data. Our main contribution is the formulation of the piecewise-planar approximation problem as a non-convex optimization problem. In turn, this problem can be efficiently solved with a graph-structured working set approach. We compare our results with a state-of-the-art region-growing-based segmentation method and show a significant improvement both in terms of approximation error and computation efficiency. Numéro de notice : A2019-592 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-2-W5-365-2019 Date de publication en ligne : 29/05/2019 En ligne : https://doi.org/10.5194/isprs-annals-IV-2-W5-365-2019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94552
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol IV-2/W5 (May 2019) . - pp 365 - 372[article]A graph-based approach for the structural analysis of road and building layouts / Mathieu Domingo in Geo-spatial Information Science, vol 22 n° 1 (March 2019)
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Titre : A graph-based approach for the structural analysis of road and building layouts Type de document : Article/Communication Auteurs : Mathieu Domingo, Auteur ; Rémy Thibaud, Auteur ; Christophe Claramunt, Auteur Année de publication : 2019 Article en page(s) : pp 59 - 72 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] bati
[Termes IGN] distance
[Termes IGN] espace urbain
[Termes IGN] graphe
[Termes IGN] réseau routierRésumé : (Auteur) A better understanding of the relationship between the structure and functions of urban and suburban spaces is one of the avenues of research still open for geographical information science. The research presented in this paper develops several graph-based metrics whose objective is to characterize some local and global structural properties that reflect the way the overall building layout can be cross-related to the one of the road layout. Such structural properties are modeled as an aggregation of parcels, buildings, and road networks. We introduce several computational measures (Ratio Minimum Distance, Minimum Ratio Minimum Distance, and Metric Compactness) that respectively evaluate the capability for a given road to be connected with the whole road network. These measures reveal emerging sub-network structures and point out differences between less-connective and more-connective parts of the network. Based on these local and global properties derived from the topological and graph-based representation, and on building density metrics, this paper proposes an analysis of road and building layouts at different levels of granularity. The metrics developed are applied to a case study in which the derived properties reveal coherent as well as incoherent neighborhoods that illustrate the potential of the approach and the way buildings and roads can be relatively connected in a given urban environment. Overall, and by integrating the parcels and buildings layouts, this approach complements other previous and related works that mainly retain the configurational structure of the urban network as well as morphological studies whose focus is generally limited to the analysis of the building layout. Numéro de notice : A2019-242 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2019.1568736 Date de publication en ligne : 15/02/2019 En ligne : https://doi.org/10.1080/10095020.2019.1568736 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92936
in Geo-spatial Information Science > vol 22 n° 1 (March 2019) . - pp 59 - 72[article]Improving LiDAR classification accuracy by contextual label smoothing in post-processing / Nan Li in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)
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Titre : Improving LiDAR classification accuracy by contextual label smoothing in post-processing Type de document : Article/Communication Auteurs : Nan Li, Auteur ; Chun Liu, Auteur ; Norbert Pfeifer, Auteur Année de publication : 2019 Article en page(s) : pp 13 - 31 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] graphe
[Termes IGN] lissage de valeur
[Termes IGN] post-traitement
[Termes IGN] précision de la classification
[Termes IGN] régularisation
[Termes IGN] scène urbaine
[Termes IGN] semis de pointsRésumé : (Auteur) We propose a contextual label-smoothing method to improve the LiDAR classification accuracy in a post-processing step. Under the framework of global graph-structured regularization, we enhance the effectiveness of label smoothing from two aspects. First, each point can collect sufficient label-relevant neighborhood information to verify its label based on an optimal graph. Second, the input label probability set is improved by probabilistic label relaxation to be more consistent with the spatial context. With this optimal graph and reliable label probability set, the final labels are computed by graph-structured regularization. We demonstrate the contextual label-smoothing approach on two separate urban airborne LiDAR datasets with complex urban scenes. Significant improvements in the classification accuracies are achieved without losing small objects (such as façades and cars). The overall accuracy is increased by 7.01% on the Vienna dataset and 6.88% on the Vaihingen dataset. Moreover, most large, wrongly labeled regions are corrected by long-range interactions that are derived from the optimal graph, and misclassified regions that lack neighborhood communications in terms of correct labels are also corrected with the probabilistic label relaxation. Numéro de notice : A2019-069 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.11.022 Date de publication en ligne : 13/12/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.11.022 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92156
in ISPRS Journal of photogrammetry and remote sensing > vol 148 (February 2019) . - pp 13 - 31[article]Réservation
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