Spatiotemporal Urban Evolution Along the China–Laos Railway in Laos Determined Using Multiple Sources of Remote-Sensed Landscape Indicators and Interpretable Machine Learning

Constructing high-speed railways (HSRs) is critical for developing countries to stimulate economic growth and urbanization. This study focuses on the Lao section of the China–Laos Railway (CLR) and employs explicitly spatial remote sensing images to investigate the urban development surrounding HSR stations. Data-driven machine learning and causal inference approaches are integrated to quantify the spatial–temporal evolution and discover its driving factors. The results suggest that the CLR has had positive spatial spillover effects on the development of the surrounding urban space. These spillover effects have exhibited a distance attenuation pattern, reflecting obvious development in 2D rather than in 3D urban space. Meanwhile, the distance to stations and adjacent city centers as well as functional urban characteristics, such as land use patterns and industrialization level, have significantly influences the surrounding spatial development. Specifically, in industrial-dominated cities, the surrounding spatial changes have been most significant under the influence of the HSR. Change related to industrial and residential land use has shown significant land expansion patterns and increased utilization efficiency, reflecting that industrialization and urbanization have been the primary drivers of land demand surrounding the HSR. The findings offer valuable insights and references for developing nations to formulate and implement spatial management policies and initiatives related to HSR.