Spatial sample weighted machine learning for multitemporal land cover change modeling with imbalanced datasets

Despite the widespread use of machine learning (ML) models for geospatial applications, adaptations to imbalanced multitemporal land cover (LC) datasets remain underexplored. For over two decades, studies have predominantly trained ML models on a single interval of LC data to model changes, with det...

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Bibliographic Details
Main Authors: Alysha van Duynhoven, Suzana Dragićević
Format: Article
Language:English
Published: Taylor & Francis Group 2025-06-01
Series:Big Earth Data
Subjects:
Online Access:https://www.tandfonline.com/doi/10.1080/20964471.2025.2518763
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