Spatiotemporal Analysis of Urban Surface Cover Structure in Ho Chi Minh City from 2015 to 2025: A Big Data and Machine Learning Approach

Land-use structure transformation in megacities such as Ho Chi Minh City (HCMC) not only reflects rapid economic growth but also constitutes a fundamental driver of geohazards, particularly land subsidence caused by increasing static and dynamic loads. To quantitatively assess this process, the study developed an automated monitoring framework on the Google Earth Engine (GEE) platform, integrating the Random Forest algorithm to process multi-temporal satellite imagery from Landsat 8/9 and Sentinel-2 over 11 years (2015–2025). Accuracy assessment results indicate robust classification performance, with Kappa coefficients ranging from 0.85 to 0.96 and Overall Accuracy between 88.1% and 97.4%. The findings reveal a clear expansion of built-up impervious surfaces, increasing from 5,500.45 ha in 2015 to 6,395.12 ha in 2025. The study successfully captured the spatiotemporal dynamics of five major land-cover classes, highlighting the pronounced growth of “built-up impervious surfaces” and the complex fluctuations of “bare land,” which reflect different construction preparation stages. Statistical analysis shows a strong spatial correlation between impervious surface expansion and areas identified as subsidence-prone. The resulting dataset provides reliable input data for geotechnical models, enabling clearer differentiation between static structural loads and dynamic traffic loads in ground deformation prediction.