High-resolution urban land use change modeling via sequential classifiers

Urban land use change models are vital tools for anticipating spatial development and its socio-economic and environmental impacts. Yet most models treat urban areas as thematically homogeneous, overlooking variation in residential and economic intensity. We present a high-resolution model for Switzerland’s densely populated Swiss Plateau (1999 settlements, hectare resolution). Using two sequential XGBoost classifiers, our model first predicts urban growth or shrinkage, then assigns one of 27 urban land use classes based on residential density, job density, and economic sector. Trained on five-year intervals (1995–2015) and validated with 2020 data, it achieves 92.3% accuracy for urban extent and a fuzzy kappa of 0.692 for class predictions. Transitions are shaped by neighborhood effects. Projections to 2050 show core cities densify most (+300 ha high density), while peri-urban and residential municipalities expand mainly at low to medium intensities (+3.7% area). Scenario testing illustrates how strategic projects reshape land use beyond intervention sites, supporting informed planning across diverse futures.