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Semantic‐based urban growth prediction / Marvin Mc Cutchan in Transactions in GIS, Vol 24 n° 6 (December 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 6 (December 2020) . - 1482 - 1503 Titre : Semantic‐based urban growth prediction Type de document : Article/Communication Auteurs : Marvin Mc Cutchan, Auteur ; Simge Özdal‐Oktay, Auteur ; Ioannis Giannopoulos, Auteur Année de publication : 2020 Article en page(s) : 1482 - 1503 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] dynamique spatiale [Termes descripteurs IGN] Europe (géographie politique) [Termes descripteurs IGN] information sémantique [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modélisation spatiale [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] organisation spatiale [Termes descripteurs IGN] OWL [Termes descripteurs IGN] prévision [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] urbanisation [Termes descripteurs IGN] ville durable Résumé : (Auteur) Urban growth is a spatial process which has a significant impact on the earth’s environment. Research on predicting this complex process makes it therefore especially fruitful for decision‐making on a global scale, as it enables the introduction of more sustainable urban development. This article presents a novel method of urban growth prediction. The method utilizes geospatial semantics in order to predict urban growth for a set of random areas in Europe. For this purpose, a feature space representing geospatial configurations was introduced which embeds semantic information. Data in this feature space was then used to perform deep learning, which ultimately enables the prediction of urban growth with high accuracy. The final results reveal that geospatial semantics hold great potential for spatial prediction tasks. Numéro de notice : A2020-766 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12655 date de publication en ligne : 14/07/2020 En ligne : https://doi.org/10.1111/tgis.12655 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96657 [article]

Semantic trajectory segmentation based on change-point detection and ontology / Yuan Gao in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 12 (December 2020) . - pp 2361 - 2394 Titre : Semantic trajectory segmentation based on change-point detection and ontology Type de document : Article/Communication Auteurs : Yuan Gao, Auteur ; Longfei Huang, Auteur ; Jun Feng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2361 - 2394 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] cible mobile [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] information sémantique [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] objet mobile [Termes descripteurs IGN] ontologie [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] probabilité [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] trajectoire Résumé : (auteur) Trajectory segmentation is a fundamental issue in GPS trajectory analytics. The task of dividing a raw trajectory into reasonable sub-trajectories and annotating them based on moving subject’s intentions and application domains remains a challenge. This is due to the highly dynamic nature of individuals’ patterns of movement and the complex relationships between such patterns and surrounding points of interest. In this paper, we present a framework called SEMANTIC-SEG for automatic semantic segmentation of trajectories from GPS readings. For the decomposition component of SEMANTIC-SEG, a moving pattern change detection (MPCD) algorithm is proposed to divide the raw trajectory into segments that are homogeneous in their movement conditions. A generic ontology and a spatiotemporal probability model for segmentation are then introduced to implement a bottom-up ontology-based reasoning for semantic enrichment. The experimental results on three real-world datasets show that MPCD can more effectively identify the semantically significant change-points in a pattern of movement than four existing baseline methods. Moreover, experiments are conducted to demonstrate how the proposed SEMANTIC-SEG framework can be applied. Numéro de notice : A2020-689 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1798966 date de publication en ligne : 04/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1798966 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96226 [article]

The position of sound in audiovisual maps: an experimental study of performance in spatial memory / Nils Siepmann in Cartographica, vol 55 n° 2 (Summer 2020)  

Public [article]  inCartographica > vol 55 n° 2 (Summer 2020) . - pp 136 - 150 Titre : The position of sound in audiovisual maps: an experimental study of performance in spatial memory Type de document : Article/Communication Auteurs : Nils Siepmann, Auteur ; Dennis Edler, Auteur ; Julian Keil, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 136 - 150 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie numérique [Termes descripteurs IGN] audiovisuel [Termes descripteurs IGN] carte cognitive [Termes descripteurs IGN] communication cartographique [Termes descripteurs IGN] document sonore [Termes descripteurs IGN] information géographique [Termes descripteurs IGN] information sémantique [Termes descripteurs IGN] mémoire [Termes descripteurs IGN] multimedia Résumé : (auteur) Digital maps are known as reliable media for communicating spatial information. People use maps to make themselves familiar with new environments and to form cognitive representations of spatial configurations and additional semantic information that are coupled with locational information. Since the mid-1990s, cartographers have explored auditory media as cartographic elements to transfer spatial information. Among the established sound variants used in multimedia cartography, speech recordings are a popular auditory tool to enrich the visual dominance of maps. The impact of auditory elements on human spatial memory has hardly been investigated so far in cartography and spatial cognition. A recent study showed that spoken object names bound to visual location markers affect performance in memory of object locations. Map users tend to make significantly smaller spatial distortion errors in the recall of object locations if these locations are coupled with auditory semantic information (place names). The present study extends this approach by examining possible effects on sound position as cues for spatial memory performance. A monaural condition, where an auditory name is presented in a spatial location corresponding to the object location, is compared with a binaural condition (of no directional cue). The results show that a monaural communication additionally improves spatial memory performance. Interestingly, the semantic information bound to an object location appears to be the driving factor in improving this effect. Numéro de notice : A2020-441 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3138/cart-2019-0008 date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.3138/cart-2019-0008 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95499 [article]

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Semantic signatures for large-scale visual localization / Li Weng in Multimedia tools and applications, vol inconnu (2020)  

Public [article]  inMultimedia tools and applications > vol inconnu (2020) Titre : Semantic signatures for large-scale visual localization Type de document : Article/Communication Auteurs : Li Weng , Auteur ; Valérie Gouet-Brunet , Auteur ; Bahman Soheilian , Auteur Année de publication : 2020 Projets : THINGS2D0 / Gouet-Brunet, Valérie Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] appariement sémantique [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image numérique [Termes descripteurs IGN] information sémantique [Termes descripteurs IGN] recherche d'image basée sur le contenu [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Visual localization is a useful alternative to standard localization techniques. It works by utilizing cameras. In a typical scenario, features are extracted from captured images and compared with geo-referenced databases. Location information is then inferred from the matching results. Conventional schemes mainly use low-level visual features. These approaches offer good accuracy but suffer from scalability issues. In order to assist localization in large urban areas, this work explores a different path by utilizing high-level semantic information. It is found that object information in a street view can facilitate localization. A novel descriptor scheme called “semantic signature” is proposed to summarize this information. A semantic signature consists of type and angle information of visible objects at a spatial location. Several metrics and protocols are proposed for signature comparison and retrieval. They illustrate different trade-offs between accuracy and complexity. Extensive simulation results confirm the potential of the proposed scheme in large-scale applications. This paper is an extended version of a conference paper in CBMI’18. A more efficient retrieval protocol is presented with additional experiment results. Numéro de notice : A2020-367 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11042-020-08992-6 date de publication en ligne : 07/05/2020 En ligne : https://doi.org/10.1007/s11042-020-08992-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95407 [article]

An IEEE value loop of human-technology collaboration in geospatial information science / Liqiu Meng in Geo-spatial Information Science, vol 23 n° 1 (March 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 1 (March 2020) . - pp 61- 67 Titre : An IEEE value loop of human-technology collaboration in geospatial information science Type de document : Article/Communication Auteurs : Liqiu Meng, Auteur Année de publication : 2020 Article en page(s) : pp 61- 67 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Information géographique [Termes descripteurs IGN] analyse géovisuelle [Termes descripteurs IGN] approche holistique [Termes descripteurs IGN] données localisées numériques [Termes descripteurs IGN] enrichissement sémantique [Termes descripteurs IGN] éthique [Termes descripteurs IGN] géographie sociale [Termes descripteurs IGN] information sémantique [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] intelligence artificielle [Termes descripteurs IGN] interface homme-machine [Termes descripteurs IGN] recherche interdisciplinaire [Termes descripteurs IGN] web sémantique Résumé : (auteur) Geosensing and social sensing as two digitalization mainstreams in big data era are increasingly converging toward an integrated system for the creation of semantically enriched digital Earth. Along with the rapid developments of AI technologies, this convergence has inevitably brought about a number of transformations. On the one hand, value-adding chains from raw data to products and services are becoming value-adding loops composed of four successive stages – Informing, Enabling, Engaging and Empowering (IEEE). Each stage is a dynamic loop for itself. On the other hand, the “human versus technology” relationship is upgraded toward a game-changing “human and technology” collaboration. The information loop is essentially shaped by the omnipresent reciprocity between humans and technologies as equal partners, co-learners and co-creators of new values. The paper gives an analytical review on the mutually changing roles and responsibilities of humans and technologies in the individual stages of the IEEE loop, with the aim to promote a holistic understanding of the state of the art of geospatial information science. Meanwhile, the author elicits a number of challenges facing the interwoven human-technology collaboration. The transformation to a growth mind-set may take time to realize and consolidate. Research works on large-scale semantic data integration are just in the beginning. User experiences of geovisual analytic approaches are far from being systematically studied. Finally, the ethical concerns for the handling of semantically enriched digital Earth cover not only the sensitive issues related to privacy violation, copyright infringement, abuse, etc. but also the questions of how to make technologies as controllable and understandable as possible for humans and how to keep the technological ethos within its constructive sphere of societal influence. Numéro de notice : A2020-163 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10095020.2020.1718004 date de publication en ligne : 23/01/2020 En ligne : https://doi.org/10.1080/10095020.2020.1718004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94823 [article]

Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data / Sukhjit Singh Sehra in Transactions in GIS, Vol 24 n° 1 (February 2020)  

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Bertin’s graphic variables and online map makers: an empirical study of maps produced by prosumers and cartographers / Natalia Ipatow in Cartographica, vol 54 n° 4 (Winter 2019)  

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SMSM: a similarity measure for trajectory stops and moves / Andre L. Lehmann in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)  

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A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements / Yingjie Hu in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

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Integration of lidar data and GIS data for point cloud semantic enrichment at the point level / Harith Aljumaily in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

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Semantic aware quality evaluation of 3D building models : Modeling and simulation / Oussama Ennafii (2019)  

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Un modèle spatiotemporel sémantique pour la modélisation de mobilités en milieu urbain / Meihan Jin in Revue internationale de géomatique, vol 28 n° 3 (juillet - septembre 2018)  

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L’opérateur de collage : Gestion de plusieurs points de vue dans un contexte spatial / Géraldine Del Mondo in Revue internationale de géomatique, vol 28 n° 3 (juillet - septembre 2018)  

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Classification of aerial photogrammetric 3D point clouds / Carlos Becker in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 5 (mai 2018)  

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Semantic enrichment of octree structured point clouds for multi‐story 3D pathfinding / Florian W. Fichtner in Transactions in GIS, vol 22 n° 1 (February 2018)  

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