Please use this identifier to cite or link to this item: https://openlibrary-repo.ecampusontario.ca/jspui/handle/123456789/1464
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dc.contributor.authorMillard, Koreen-
dc.contributor.authorKnudby, Anders-
dc.contributor.otherSchultz, Samantha-
dc.contributor.otherDarling, Samantha-
dc.contributor.otherScott, Phillip-
dc.contributor.otherThambimuthu, Thanisan-
dc.contributor.otherCizek, Erika-
dc.contributor.otherHojjatzadeh, Negin-
dc.contributor.otherRichardson, Elisha-
dc.contributor.otherWierdsma, Matthew-
dc.contributor.otherSauro, Claudia-
dc.contributor.otherRamey, Marisa-
dc.contributor.otherGeorge, Genevieve-
dc.contributor.otherMohuiddin, Adam-
dc.contributor.otherSchatkowsky, Mat-
dc.contributor.otherGorra, Andrea-
dc.date.accessioned2022-06-17T17:22:55Z-
dc.date.available2022-06-17T17:22:55Z-
dc.date.issued2022-
dc.identifier6b455460-ebae-4fec-9322-0632570bb45c-
dc.identifier.urihttps://openlibrary-repo.ecampusontario.ca/jspui/handle/123456789/1464-
dc.description.sponsorshipThis project is made possible with funding by the Government of Ontario and through eCampusOntario’s support of the Virtual Learning Strategy.en_US
dc.language.isoengen_US
dc.rightsCC BY-NC-SA | https://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.subjectGeographic information systems (GIS)en_US
dc.subjectGeomaticsen_US
dc.subjectRemote sensingen_US
dc.titleBig Data Remote Sensingen_US
dc.typeLearning Objecten_US
dcterms.accessRightsOpen Accessen_US
dcterms.accessRightsOpen Access-
dcterms.educationLevelUniversity - Undergraduateen_US
dcterms.educationLevelUniversity - Graduate & Post-Graduateen_US
dc.identifier.slughttps://openlibrary.ecampusontario.ca/catalogue/item/?id=6b455460-ebae-4fec-9322-0632570bb45c-
ecO-OER.AdoptedNoen_US
ecO-OER.AncillaryMaterialNoen_US
ecO-OER.InstitutionalAffiliationCarleton Universityen_US
ecO-OER.ISNI0000 0004 1936 893Xen_US
ecO-OER.ReviewedNoen_US
ecO-OER.AccessibilityStatementYesen_US
lrmi.learningResourceTypeEducational Unit - Courseen_US
lrmi.learningResourceTypeEducational Unit - Laben_US
lrmi.learningResourceTypeInstructional Object - Lecture Materialen_US
lrmi.learningResourceTypeAssessment - Question Bank/Problem Seten_US
ecO-OER.POD.compatibleNoen_US
dc.description.abstractThis course includes advanced topics in remote sensing using open-access tools and freely-accessible data. The focus of this course is on understanding and applying concepts in “big geospatial data analysis” to large-area and time series analysis problems. These techniques will allow students to analyze environmental conditions and phenomena using remotely-sensed imagery and perform spatial and statistical analysis. Students will be able to explore solutions to problems related to their own interests or objectives through an independent project. This course uses Google Earth Engine, a cloud based remote sensing progressing suite. Students will require access to the internet through a modern browser and a Google account (i.e. Gmail, Google Drive). No coding skills are required but students will be expected to use and develop their own Python scripts in the labs.en_US
dc.subject.otherSciences - Earth Sciencesen_US
dc.subject.otherSocial Sciences - Geographyen_US
dc.subject.otherTechnology - Computer Scienceen_US
dc.subject.otherTechnology - Research & Dataen_US
ecO-OER.VLS.projectIDCARL-1037en_US
ecO-OER.VLS.CategoryDigital Content - Create a New Online Courseen_US
ecO-OER.VLSYesen_US
ecO-OER.CVLPNoen_US
ecO-OER.ItemTypeCourseen_US
ecO-OER.MediaFormatCommon Cartridgeen_US
ecO-OER.VLS.cvlpSupportedNoen_US
Appears in Collections:Ontario OER Collection
VLS Collection

Files in This Item:
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BigDataRemoteSensing-CommonCartridge01.zip%%dl%% Common Cartridge503.86 MBzipView/Open
BigDataRemoteSensing-CommonCartridgeThin.zip%%dl%% Common Cartridge Thin24.35 kBzipView/Open
BigDataRemoteSensing-Accessibility Review.docx%%acc%% Accessibility Statement6.49 kBMicrosoft WordView/Open


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