| Title: | Data mining and machine learning in earth observation |
| Authors: | Alexandria Farias, Author |
| Material Type: | ISU Individual Project |
| Publisher: | Illkirch-Graffenstaden (France) : International Space University, 2019 |
| Size: | 1 electronic resource (vii, 32 p.) / col. ill. |
| Bibliography note: | Includes bibliographical references |
| Languages: | English |
| Description: |
The main challenges for earth observation, including the size of data, its complex nature, a high barrier to entry, and the datasets used for training data, are discussed, as well as the solutions that are addressing these challenges.
This paper will show how some of these techniques are currently being used in the field of earth observation. The Google Earth Engine (EE) has been chosen to process and run our scripts on publically available Landsat-7 remote sensing (RS) data catalogs. Using this RS data, it is possible to classify and discover historical algal blooms in the Baltic Sea surrounding the Swedish island of Gotland. |
| ISU program : | Master of Space Studies |
| Permalink: | https://isulibrary.isunet.edu/index.php?lvl=notice_display&id=10704 |
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 Farias, Alexandria_IPR (2.59MB) Adobe Acrobat PDF |
