ART-P-MAP neural networks modeling of land-use change: accounting for spatial heterogeneity and uncertainty

Spatial land-use models over large geographic areas and at fine spatial resolutions face the challenges of spatial heterogeneity, model predictability, data quality, and of the ensuing uncertainty. We propose an improved neural network model, ART-Probability-Map (ART-P-MAP), tailored to address these issues in the context of spatial modeling of land-use change. First, it adaptively forms its own network structure to account for spatial heterogeneity. Second, it explicitly infers posterior probabilities of land conversion that facilitates the quantification of prediction uncertainty. Extensive calibration under various test settings is conducted on the proposed model to optimize its utility in seeking useful information within a spatially heterogeneous environment. The calibration strategy involves building a bagging ensemble for training and stratified sampling with varying category proportions for experimentation. Through a temporal validation approach, we examine models’ performance within a systematic assessment framework consisting of global metrics and cell-level uncertainty measurement. Compared with two baselines, ART-P-MAP achieves consistently good and stable performance across experiments and exhibits superior capability to handle the spatial heterogeneity and uncertainty involved in the land-use change problem. Finally, we conclude that, as a general probabilistic regression model, ART-P-MAP is applicable to a broad range of land-use change modeling approaches, which deserves future research.