AI-Enhanced Urban Cognition: ConvLSTM Networks for Multi-Scenario Land Cover Prediction in Metropolitan Regions

Abstract This study presents a transformative approach to urban development prediction by integrating Convolutional Long Short-Term Memory (ConvLSTM) neural networks with scenario-based planning frameworks. Unlike traditional land use prediction methods that treat spatial and temporal dimensions separately, this interred deep learning and scenario planning architecture simultaneously processes both dimensions, revealing complex spatiotemporal patterns in urban growth across urban regions. The application of the framework is demonstrated in the Colorado's Metropolitan Planning Organization areas for stakeholder-driven alternative future scenarios ranging from business-as-usual to hot-growth. Analysis of 39 consecutive years of land cover data (1985–2023) reveals a counterintuitive "Temporal Depth Paradox" where prediction accuracy increases with temporal distance, challenging fundamental assumptions in urban forecasting. The model effectively distinguishes between different development processes, showing higher predictive accuracy for greenfield expansion versus complex urban redevelopment. By embedding ecological constraints and evaluating five distinct growth scenarios, we demonstrate how AI can support sustainable development goals while maintaining human oversight in planning processes. Quantitative analysis reveals that the Adaptive Innovation scenario produces significantly improved infrastructure efficiency while preserving approximately 1,000 additional hectares of natural areas by 2050 compared to unregulated scenarios. This research advances beyond mere enhancement of existing methodologies to create new frameworks for urban cognition that can identify optimal development patterns aligned with sustainability objectives. The findings provide planners with evidence-based tools to evaluate how different policy interventions might reshape urban morphology, enhance infrastructure efficiency, and preserve natural resources in rapidly growing regions.