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...

詳細記述

書誌詳細
主要な著者: Usman Ali, Travis J. Esau, Aitazaz A. Farooque, Qamar U. Zaman, Farhat Abbas, Mathieu F. Bilodeau
フォーマット: 論文
言語:English
出版事項: MDPI AG 2022-06-01
シリーズ:ISPRS International Journal of Geo-Information
主題:
オンライン・アクセス:https://www.mdpi.com/2220-9964/11/6/333