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GIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data / Wanqin He in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 6 (June 2022) . - n° 336 Titre : GIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data Type de document : Article/Communication Auteurs : Wanqin He, Auteur ; Sara Shirowzhan, Auteur ; Christopher Pettit, Auteur Année de publication : 2022 Article en page(s) : n° 336 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse diachronique [Termes IGN] apprentissage automatique [Termes IGN] brousse [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] coefficient de corrélation [Termes IGN] données météorologiques [Termes IGN] données spatiotemporelles [Termes IGN] humidité du sol [Termes IGN] incendie [Termes IGN] indice de végétation [Termes IGN] Nouvelle-Galles du Sud [Termes IGN] prévention des risques [Termes IGN] régression linéaire [Termes IGN] Spark [Termes IGN] système d'information géographique [Termes IGN] température de l'air Résumé : (auteur) The causes of bushfires are extremely complex, and their scale of burning and probability of occurrence are influenced by the interaction of a variety of factors such as meteorological factors, topography, human activity and vegetation type. An in-depth understanding of the combined mechanisms of factors affecting the occurrence and spread of bushfires is needed to support the development of effective fire prevention plans and fire suppression measures and aid planning for geographic, ecological maintenance and urban emergency management. This study aimed to explore how bushfires, meteorological variability and other natural factors have interacted over the past 40 years in NSW Australia and how these influencing factors synergistically drive bushfires. The CSIRO’s Spark toolkit has been used to simulate bushfire burning spread over 24 h. The study uses NSW wildfire data from 1981–2020, combined with meteorological factors (temperature, precipitation, wind speed), vegetation data (NDVI data, vegetation type) and topography (slope, soil moisture) data to analyse the relationship between bushfires and influencing factors quantitatively. Machine learning-random forest regression was then used to determine the differences in the influence of bushfire factors on the incidence and burn scale of bushfires. Finally, the data on each influence factor was imported into Spark, and the results of the random forest model were used to set different influence weights in Spark to visualise the spread of bushfires burning over 24 h in four hotspot regions of bushfire in NSW. Wind speed, air temperature and soil moisture were found to have the most significant influence on the spread of bushfires, with the combined contribution of these three factors exceeding 60%, determining the spread of bushfires and the scale of burning. Precipitation and vegetation showed a greater influence on the annual frequency of bushfires. In addition, burn simulations show that wind direction influences the main direction of fire spread, whereas the shape of the flame front is mainly due to the influence of land classification. Besides, the simulation results from Spark could predict the temporal and spatial spread of fire, which is a potential decision aid for fireproofing agencies. The results of this study can inform how fire agencies can better understand fire occurrence mechanisms and use bushfire prediction and simulation techniques to support both their operational (short-term) and strategic (long-term) fire management responses and policies. Numéro de notice : A2022-481 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11060336 Date de publication en ligne : 05/06/2022 En ligne : https://doi.org/10.3390/ijgi11060336 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100894 [article]

Artificial intelligence / Eneko Osaba (2021)  

Public Titre : Artificial intelligence : Latest advances, new paradigms and novel applications Type de document : Monographie Auteurs : Eneko Osaba, Auteur ; Esther Villar-Rodriguez, Auteur ; Jesus L. Lobo, Auteur ; et al., Auteur Editeur : London [UK] : IntechOpen Année de publication : 2021 Importance : 158 p. Format : 16 x 23 cm ISBN/ISSN/EAN : 978-1-83962-389-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage profond [Termes IGN] exploration de données [Termes IGN] innovation [Termes IGN] modèle orienté agent [Termes IGN] organisme international [Termes IGN] reconnaissance de formes [Termes IGN] règlement [Termes IGN] réseau neuronal artificiel [Termes IGN] Spark [Termes IGN] système d'information Résumé : (éditeur) Artificial Intelligence (AI) is widely known as a knowledge field that aims to make computers, robots, or products that mimic the way humans think. In the current scientific community, AI is an intensively studied area composed of multiple branches. Historically, machine learning and optimization are two of the most studied fronts thanks to the development of novel and challenging research topics such as transfer optimization, swarm robotics, and drift detection and adaptation to evolving conditions in real-time. This book collects radically new theoretical insights, reporting recent developments and evincing innovative applications regarding AI methods in all fields of knowledge. It also presents works focused on new paradigms and novel branches of AI science. Note de contenu : 1- Introductory chapter: Artificial intelligence - Latest advances, new paradigms and novel applications 2- Big data framework using Spark architecture for dose optimization based on deep learning in medical imaging 3- Novelty detection methodology based on self-organizing maps for power quality monitoring 4- AI-powered workforce management and its future in India 5- Agent based load balancing in grid computing 6- A food recommender based on frequent sets of food mining using image recognition 7- The prospects for creating instruments for the coordination of activities of international organizations in the regulation of artificial intelligence 8- Artificial intelligence assisted innovation 9- Quest for I (intelligence) in AI (artificial intelligence): A non-elusive attempt Numéro de notice : 28633 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87770 En ligne : https://doi.org/10.5772/intechopen.87770 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99636

Efficiently distributed watertight surface reconstruction / Laurent Caraffa (2021)  

Public Titre : Efficiently distributed watertight surface reconstruction Type de document : Article/Communication Auteurs : Laurent Caraffa , Auteur ; Yanis Marchand , Auteur ; Mathieu Brédif , Auteur ; Bruno Vallet , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2021 Projets : 1-Pas de projet / Conférence : 3DV 2021, International Conference on 3D Vision 01/12/2021 03/12/2021 Londres online Royaume-Uni Proceedings IEEE Importance : pp 1432 - 1441 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme Graph-Cut [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] reconstruction d'objet [Termes IGN] scène [Termes IGN] semis de points [Termes IGN] Spark [Termes IGN] triangulation de Delaunay Résumé : (auteur) We present an out-of-core and distributed surface reconstruction algorithm which scales efficiently on arbitrarily large point clouds (with optical centres) and produces a 3D watertight triangle mesh representing the surface of the underlying scene. Surface reconstruction from a point cloud is a difficult problem and existing state of the art approaches are usually based on complex pipelines making use of global algorithms (i.e. Delaunay triangulation, graph-cut optimisation). For one of these approaches, we investigate the distribution of all the steps (in particular Delaunay triangulation and graph-cut optimisation) in order to propose a fully scalable method. We show that the problem can be tiled and distributed across a cloud or a cluster of PCs by paying a careful attention to the interactions between tiles and using Spark computing framework. We confirm the efficiency of this approach with an in-depth quantitative evaluation and the successful reconstruction of a surface from a very large data set which combines more than 350 million aerial and terrestrial LiDAR points. Numéro de notice : C2021-037 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/3DV53792.2021.00150 En ligne : https://doi.org/10.1109/3DV53792.2021.00150 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99167

Flex-ER: A platform to evaluate interaction techniques for immersive visualizations / María-Jesús Lobo in Proceedings of the ACM on Human-Computer Interaction, Vol 4 (November 2020)  

Public [article]  inProceedings of the ACM on Human-Computer Interaction > Vol 4 (November 2020) . - n° 195 Titre : Flex-ER: A platform to evaluate interaction techniques for immersive visualizations Type de document : Article/Communication Auteurs : María-Jesús Lobo , Auteur ; Christophe Hurter, Auteur ; Pourang Polad Irani, Auteur Année de publication : 2020 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : n° 195 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] environnement de développement [Termes IGN] format JSON [Termes IGN] plateforme logicielle [Termes IGN] visualisation de données [Termes IGN] vue immersive [Vedettes matières IGN] Géovisualisation Mots-clés libres : Flex-ER Résumé : (auteur) Extended Reality (XR) systems (which encapsulate AR, VR and MR) is an emerging field which enables the development of novel visualization and interaction techniques. To develop and to assess such techniques, researchers and designers have to face choices in terms of which development tools to adopt, and with very little information about how such tools support some of the very basic tasks for information visualization, such as selecting data items, linking and navigating. As a solution, we propose Flex-ER, a flexible web-based environment that enables users to prototype, debug and share experimental conditions and results. Flex-ER enables users to quickly switch between hardware platforms and input modalities by using a JSON specification that supports both defining interaction techniques and tasks at a low cost. We demonstrate the flexibility of the environment through three task design examples: brushing, linking and navigating. A qualitative user study suggest that Flex-ER can be helpful to prototype and explore different interaction techniques for immersive analytics. Numéro de notice : A2020-818 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1145/3427323 En ligne : https://doi.org/10.1145/3427323 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97091 [article]

Provably consistent distributed Delaunay triangulation / Mathieu Brédif in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2020 (August 2020) . - pp 195 - 202 Titre : Provably consistent distributed Delaunay triangulation Type de document : Article/Communication Auteurs : Mathieu Brédif , Auteur ; Laurent Caraffa , Auteur ; Murat Yirci, Auteur ; Pooran Memari, Auteur Année de publication : 2020 Projets : IQmulus / Métral, Claudine Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Article en page(s) : pp 195 - 202 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] géomètrie algorithmique [Termes IGN] informatique en nuage [Termes IGN] semis de points [Termes IGN] Spark [Termes IGN] traitement de semis de points [Termes IGN] triangulation de Delaunay Résumé : (Auteur) This paper deals with the distributed computation of Delaunay triangulations of massive point sets, mainly motivated by the needs of a scalable out-of-core surface reconstruction workflow from massive urban LIDAR datasets. Such a data often corresponds to a huge point cloud represented through a set of tiles of relatively homogeneous point sizes. This will be the input of our algorithm which will naturally partition this data across multiple processing elements. The distributed computation and communication between processing elements is orchestrated efficiently through an uncentralized model to represent, manage and locally construct the triangulation corresponding to each tile. Initially inspired by the star splaying approach, we review the Tile\& Merge algorithm for computing Distributed Delaunay Triangulations on the cloud, provide a theoretical proof of correctness of this algorithm, and analyse the performance of our Spark implementation in terms of speedup and strong scaling in both synthetic and real use case datasets. A HPC implementation (e.g. using MPI), left for future work, would benefit from its more efficient message passing paradigm but lose the robustness and failure resilience of our Spark approach. Numéro de notice : A2020-410 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-195-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-195-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94979 [article]

Distributed and parallel architectures for spatial data / Alberto Belussi (2020)  

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Big data security / Shibakali Gupta (2019)  

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Contribution au développement d’une plateforme web d’analyse réglementaire et de gestion des vols de drones / Yassmine Boudili (2019) 

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Développement de la plateforme ApeX Web / Paul Mazeran (2019) 

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Spatial data management in apache spark: the GeoSpark perspective and beyond / Jia Yu in Geoinformatica, vol 23 n° 1 (January 2019)  

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Tile & merge: Distributed Delaunay triangulations for cloud computing / Laurent Caraffa (2019)  

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Opening GIScience : A process-based approach / Jerry Shannon in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

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SensePlace3: a geovisual framework to analyze place–time–attribute information in social media / Scott Pezanowski in Cartography and Geographic Information Science, Vol 45 n° 5 (August 2018)  

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Développement pour l’interface Qgis d’Hydra, logiciel de modélisation hydraulique / Maximilien Jaffrès (2018) 

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A simulation and visualization environment for spatiotemporal disaster risk assessments of network infrastructures / Magnus Heittzler in Cartographica, vol 52 n° 4 (Winter 2017)  

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