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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Dettagli Bibliografici
Autori principali:	Usman Ali, Travis J. Esau, Aitazaz A. Farooque, Qamar U. Zaman, Farhat Abbas, Mathieu F. Bilodeau
Natura:	Articolo
Lingua:	English
Pubblicazione:	MDPI AG 2022-06-01
Serie:	ISPRS International Journal of Geo-Information
Soggetti:	remote sensing indices machine learning ground truth data LULC mapping satellite imagery
Accesso online:	https://www.mdpi.com/2220-9964/11/6/333

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