Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Meek, Sam"'
Publikováno v:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI, vol. 13051, pp. 380-390. SPIE, 2024
Combined, joint, intra-governmental, inter-agency and multinational (CJIIM) operations require rapid data sharing without the bottlenecks of metadata curation and alignment. Curation and alignment is particularly infeasible for external open source i
Externí odkaz:
http://arxiv.org/abs/2408.10734
Publikováno v:
In Computers and Geosciences February 2016 87:76-83
Publikováno v:
In Computers and Geosciences March 2013 52:334-344
This document describes the LandSense Quality Assurance (QA) services that are deployed on the LandSense Engagement Platform (LEP - https://lep.landsense.eu/). The motivating work for deployment of the services was carried out in the European Union F
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94b325eef8ea4ebe93c21e041b3e477c
Autor:
Schwartz, Peter J., Jensen, Benjamin, Hohil, Myron E., Davies, Cai, Meek, Sam, Hawkins, Philip, Tutcher, Benomy, Bent, Graham, Preece, Alun
Publikováno v:
Proceedings of SPIE; June 2024, Vol. 13051 Issue: 1 p1305118-1305118-11
Akademický článek
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Publikováno v:
Transactions in GIS. Aug2013, Vol. 17 Issue 4, p526-543. 18p. 10 Color Photographs, 1 Black and White Photograph, 4 Charts.
Publikováno v:
Repositori Universitat Jaume I
Universitat Jaume I
Universitat Jaume I
Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014. Crowdsourcing as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::1373e19369fe3591f970fb92c70d70a9
http://hdl.handle.net/10234/98927
http://hdl.handle.net/10234/98927
Publikováno v:
11th World Conference on Mobile and Contextual Learning (mLearn 2012)
A successful application area of mobile technology for learning has been to provide location-based guides that inform students or tourists about their immediate surroundings. In this paper we extend this location-based learning to the distant landsca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=core_ac_uk__::382d626262d1d90fc0f485d9166cbc90
http://oro.open.ac.uk/35303/1/Preprint_Sharples_et_al_mLearn_2012.pdf
http://oro.open.ac.uk/35303/1/Preprint_Sharples_et_al_mLearn_2012.pdf