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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς:	Usman Ali, Travis J. Esau, Aitazaz A. Farooque, Qamar U. Zaman, Farhat Abbas, Mathieu F. Bilodeau
Μορφή:	Άρθρο
Γλώσσα:	English
Έκδοση:	MDPI AG 2022-06-01
Σειρά:	ISPRS International Journal of Geo-Information
Θέματα:	remote sensing indices machine learning ground truth data LULC mapping satellite imagery
Διαθέσιμο Online:	https://www.mdpi.com/2220-9964/11/6/333

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