The Future of EO-Enabled SDG 11.3.1 Monitoring Through Uncertainty-Aware Forecasting of Urban Land-Use Efficiency

Jojene Santillan, Mareike Dorozynski, Christian Heipke

Earth observation (EO) has become central to monitoring progress toward the Sustainable Development Goals (SDGs), particularly SDG Indicator 11.3.1, which assesses land-use efficiency (LUE) through the ratio of land consumption rate (LCR) to population growth rate (PGR). Current EO-based implementations remain predominantly retrospective and deterministic, relying on historical mappings of built-up area and population. However, decision-making for sustainable urban development increasingly requires forward-looking and uncertainty-aware information. This paper argues that the future of EO-enabled SDG monitoring lies in uncertainty-aware forecasting rather than deterministic retrospective assessment. We present a global EO-driven framework that integrates deep-learning-based forecasting of built-up area and population with Monte Carlo uncertainty propagation to derive probabilistic projections of SDG Indicator 11.3.1. Using multi-decadal EO-derived time series for 8,478 urban centres worldwide, we demonstrate how forecast uncertainty is structurally transmitted through SDG Indicator 11.3.1 and how deterministic forecasts can mask substantial uncertainty in future LUE classifications. The results highlight forecasting and uncertainty quantification as essential components of next-generation EO analytics for sustainable development.