Urbanization is a major driver of economic development but also intensifies pressure on land resources, making sustainable urban growth an important challenge for rapidly developing regions. This study proposes an integrated framework for assessing Sustainable Development Goal (SDG) Indicator 11.3.1 (Land Use Efficiency, LUE) by combining multi-source satellite imagery, machine learning, the Degree of Urbanization (DEGURBA) framework, and official population statistics. Bali Province, Indonesia, was selected as the study area due to its rapid tourism-driven urbanization and strategic role as one of the country's metropolitan regions. Multi-temporal land cover maps for 2010, 2015, and 2020 were generated using the Hist-Gradient Boosting (HGB) classifier, which achieved overall accuracies of 80.9%, 84.8%, and 85.9%, respectively. The resulting built-up maps were integrated with gridded population data to derive Land Consumption Rate (LCR), Population Growth Rate (PGR), and LUE at the subdistrict level based on the DEGURBA classification. The results indicate that built-up area expanded by approximately 71.2% during 2010–2020, while the overall urban hierarchy remained relatively stable, suggesting that urban growth primarily occurred through the densification and outward expansion of existing urban centers. Furthermore, the analysis demonstrates that LUE should be interpreted together with its constituent indicators, as exceptionally high or negative LUE values were largely associated with variations in population growth rather than built-up area change. The proposed framework provides a reproducible and spatially explicit approach for integrating Earth observation data with official statistics to support SDG 11.3.1 monitoring and evidence-based urban planning in Indonesia.
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