A Deep Learning Framework for Forecasting Built-Up Area and Population to Support Land Use Efficiency Projections under SDG 11.3.1

Abstract. Sustainable Development Goal (SDG) 11.3.1 monitors land use efficiency (LUE) through the relationship between the land consumption rate (LCR) and the population growth rate (PGR), yet current applications have largely remained retrospective and offer limited foresight for policy and planning. To address this gap, this study develops a scalable probabilistic deep learning (DL)-based framework that jointly forecasts built-up area (BU) and population (Pop) and propagates these forecasts into LCR, PGR, and their ratio (LCRPGR). The framework is based on a lightweight hybrid model composed of one-dimensional convolutional neural networks (Conv1D) and a recurrent network with Long Short-Term Memory (LSTM) units, trained using quantile loss to produce interval-bounded probabilistic forecasts. Ensemble aggregation was used to improve robustness, with a calibration stage included to ensure reliable coverage of prediction intervals. Applied to 8,478 urban centres using historical built-up area and population data from the Global Human Settlement Layer – Urban Centre Database (GHS-UCDB) 2025 edition, the framework generated probabilistic forecasts for the period 2025-2030, with prediction intervals shown to be well-calibrated on historical data. Results indicate that total built-up area is projected to expand from 108,808 km² in 2020 to 118,123 km² by 2030 (+8.6%), while population is expected to grow from 2.875 to 3.159 billion (+9.9%). At the aggregated level, LCRPGR values suggest a general trend toward efficiency, with PGR slightly outpacing LCR, though uncertainty intervals cross the efficiency threshold. While the implementation addressed the constraints of SDG 11.3.1 forecasting (short time series, limited covariates, and large-scale application), the framework remains flexible. It can be extended with denser time series, richer covariates, and spatiotemporal architectures. This study offers a practical basis for near-term LUE monitoring and risk-aware policy action as 2030 approaches by providing reproducible and uncertainty-aware forecasts.