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An Analysis of Fast Learning Methods for Classifying Forest Cover Types
Department of Global Public Health Sciences, Karolinska Institutet, Solna, Sweden; Department ofStatistics, Örebro University, Örebro, Sweden.
Örebro University, School of Science and Technology. Department of Computer Science.ORCID iD: 0000-0002-0579-7181
Örebro University, Örebro University School of Business. Department of Statistics.ORCID iD: 0000-0002-1488-4703
2020 (English)In: Applied Artificial Intelligence, ISSN 0883-9514, E-ISSN 1087-6545, Vol. 34, no 10, p. 691-709Article in journal (Refereed) Published
Abstract [en]

Proper mapping and classification of Forest cover types are integral in understanding the processes governing the interaction mechanism of the surface with the atmosphere. In the presence of massive satellite and aerial measurements, a proper manual categorization has become a tedious job. In this study, we implement three different modest machine learning classifiers along with three statistical feature selectors to classify different cover types from cartographic variables. Our results showed that, among the chosen classifiers, the standard Random Forest Classifier together with Principal Components performs exceptionally well, not only in overall assessment but across all seven categories. Our results are found to be significantly better than existing studies involving more complex Deep Learning models.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2020. Vol. 34, no 10, p. 691-709
National Category
Other Natural Sciences Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-83354DOI: 10.1080/08839514.2020.1771523ISI: 000550104300001Scopus ID: 2-s2.0-85086860115OAI: oai:DiVA.org:oru-83354DiVA, id: diva2:1443468
Funder
The Jan Wallander and Tom Hedelius Foundation, P18-0201Available from: 2020-06-18 Created: 2020-06-18 Last updated: 2020-08-19Bibliographically approved

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Längkvist, MartinJaved, Farrukh

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CiteExportLink to record
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