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