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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Bibliografische gegevens
Hoofdauteurs: Usman Ali, Travis J. Esau, Aitazaz A. Farooque, Qamar U. Zaman, Farhat Abbas, Mathieu F. Bilodeau
Formaat: Artikel
Taal:English
Gepubliceerd in: MDPI AG 2022-06-01
Reeks:ISPRS International Journal of Geo-Information
Onderwerpen:
remote sensing indices
machine learning
ground truth data
LULC mapping
satellite imagery
Online toegang:https://www.mdpi.com/2220-9964/11/6/333

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

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