Détail de l'auteur
Auteur Michael E. Hodgson |
Documents disponibles écrits par cet auteur (2)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externes
Parallel cartographic modeling: a methodology for parallelizing spatial data processing / Eric Shook in International journal of geographical information science IJGIS, vol 30 n° 11-12 (November - December 2016)
[article]
Titre : Parallel cartographic modeling: a methodology for parallelizing spatial data processing Type de document : Article/Communication Auteurs : Eric Shook, Auteur ; Michael E. Hodgson, Auteur ; Shaowen Wang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 2355 - 2376 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] langage de programmation
[Termes IGN] Map Algebra
[Termes IGN] modèle conceptuel de données localisées
[Termes IGN] Python (langage de programmation)
[Termes IGN] traitement de données localisées
[Termes IGN] traitement parallèle
[Termes IGN] WebSIGRésumé : (Auteur) This article establishes a new methodological framework for parallelizing spatial data processing called parallel cartographic modeling, which extends the widely adopted cartographic modeling framework. Parallel cartographic modeling adds a novel component called a Subdomain, which serves as the elemental unit of parallel computation. Four operators are also added to express parallel spatial data processing, namely scheduler, decomposition, executor, and iteration. A parallel cartographic modeling language (PCML) is developed based on the parallel cartographic modeling framework, which is designed for usability, programmability, and scalability. PCML is a domain-specific language implemented in Python for the domain of cyberGIS. A key feature of PCML is that it supports automatic parallelization of cartographic modeling scripts; thus, allowing the analyst to develop models in the familiar cartographic modeling language in a Python syntax. PCML currently supports more than 70 operations and new operations can be easily implemented in as little as three lines of PCML code. Experimental results using the National Science Foundation-supported Resourcing Open Geospatial Education and Research computational resource demonstrate that PCML efficiently scales to 16 cores and can process gigabytes of spatial data in parallel. PCML is shown to support multiple decomposition strategies, decomposition granularities, and iteration strategies that be generically applied to any operation implemented in PCML. Numéro de notice : A2016-755 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1172714 En ligne : http://dx.doi.org/10.1080/13658816.2016.1172714 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82420
in International journal of geographical information science IJGIS > vol 30 n° 11-12 (November - December 2016) . - pp 2355 - 2376[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2016061 RAB Revue Centre de documentation En réserve L003 Disponible Satellite image collection modeling for large area hazard emergency response / Shufan Liu in ISPRS Journal of photogrammetry and remote sensing, vol 118 (August 2016)
[article]
Titre : Satellite image collection modeling for large area hazard emergency response Type de document : Article/Communication Auteurs : Shufan Liu, Auteur ; Michael E. Hodgson, Auteur Année de publication : 2016 Article en page(s) : pp 13 – 21 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] cartographie d'urgence
[Termes IGN] image satellite
[Termes IGN] planification
[Termes IGN] risque naturel
[Termes IGN] traitement d'imageRésumé : (auteur) Timely collection of critical hazard information is the key to intelligent and effective hazard emergency response decisions. Satellite remote sensing imagery provides an effective way to collect critical information. Natural hazards, however, often have large impact areas – larger than a single satellite scene. Additionally, the hazard impact area may be discontinuous, particularly in flooding or tornado hazard events. In this paper, a spatial optimization model is proposed to solve the large area satellite image acquisition planning problem in the context of hazard emergency response. In the model, a large hazard impact area is represented as multiple polygons and image collection priorities for different portion of impact area are addressed. The optimization problem is solved with an exact algorithm. Application results demonstrate that the proposed method can address the satellite image acquisition planning problem. A spatial decision support system supporting the optimization model was developed. Several examples of image acquisition problems are used to demonstrate the complexity of the problem and derive optimized solutions. Numéro de notice : A2016-588 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.04.007 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.04.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81733
in ISPRS Journal of photogrammetry and remote sensing > vol 118 (August 2016) . - pp 13 – 21[article]