Please use this identifier to cite or link to this item:
https://openlibrary-repo.ecampusontario.ca/jspui/handle/123456789/1464
Title: | Big Data Remote Sensing |
Authors: | Millard, Koreen Knudby, Anders Schultz, Samantha Darling, Samantha Scott, Phillip Thambimuthu, Thanisan Cizek, Erika Hojjatzadeh, Negin Richardson, Elisha Wierdsma, Matthew Sauro, Claudia Ramey, Marisa George, Genevieve Mohuiddin, Adam Schatkowsky, Mat Gorra, Andrea |
Keywords: | Geographic information systems (GIS) Geomatics Remote sensing |
Issue Date: | 2022 |
Abstract: | This 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. |
URI: | https://openlibrary-repo.ecampusontario.ca/jspui/handle/123456789/1464 |
Other Identifiers: | 6b455460-ebae-4fec-9322-0632570bb45c |
Appears in Collections: | Ontario OER Collection VLS Collection |
Files in This Item:
File | Description | Size | Format | |
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BigDataRemoteSensing-CommonCartridge01.zip | %%dl%% Common Cartridge | 503.86 MB | zip | View/Open |
BigDataRemoteSensing-CommonCartridgeThin.zip | %%dl%% Common Cartridge Thin | 24.35 kB | zip | View/Open |
BigDataRemoteSensing-Accessibility Review.docx | %%acc%% Accessibility Statement | 6.49 kB | Microsoft Word | View/Open |
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