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

<p dir="ltr">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 gro...

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Bibliographic Details
Main Author: Usman Ali (6586886) (author)
Other Authors: Travis J. Esau (17541300) (author), Aitazaz A. Farooque (17541303) (author), Qamar U. Zaman (8060156) (author), Farhat Abbas (5480) (author), Mathieu F. Bilodeau (17542110) (author)
Published: 2022
Subjects:
Engineering
Geomatic engineering
Information and computing sciences
Data management and data science
Machine learning
remote sensing indices
machine learning
ground truth data
LULC mapping
satellite imagery
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