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 detriments of imbalanced training datasets managed through manual manipulations. Therefore, this study proposes and implements an ML-spatial sample weighting (ML-SSW) approach to leverage available multitemporal LC data while adjusting sample influence to reflect recency of change occurrence and class-level spatial pattern measures to enable data-driven LC change modeling. Random Forest (RF), Neural Network (NN), and Extreme Gradient Boosting Machine (XGB) models are trained under the ML-SSW strategy on three study areas located in British Columbia, Canada. The RF-SSW, NN-SSW, and XGB-SSW models forecasted more realistic changes across multiple timesteps with fewer errors than baseline configurations. The presented methodology provides a step toward establishing spatialized cost-sensitive learning strategies and extending classical ML models to multitemporal LC datasets.