Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms

Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms

Land use and land cover (LULC) classification maps help understand the state and trends of agricultural production and provide insights for applications in environmental monitoring. One of the major downfalls of the LULC technique is inherently linked to its need for ground truth data to cross-valid...

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מידע ביבליוגרפי
Main Authors: Usman Ali, Travis J. Esau, Aitazaz A. Farooque, Qamar U. Zaman, Farhat Abbas, Mathieu F. Bilodeau
פורמט: Article
שפה:English
יצא לאור: MDPI AG 2022-06-01
סדרה:ISPRS International Journal of Geo-Information
נושאים:
remote sensing indices
machine learning
ground truth data
LULC mapping
satellite imagery
גישה מקוונת:https://www.mdpi.com/2220-9964/11/6/333

אינטרנט

https://www.mdpi.com/2220-9964/11/6/333

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