Analyzing Urban Expansion in Hanoi Using Machine Learning and Multi-Temporal Satellite Imagery

The rapid urban expansion and development of Hanoi, the capital of Vietnam, in recent times have presented challenges for managers in monitoring, evaluating, and rationally managing natural resources. This study monitors urban expansion in the centre of Hanoi using GIS and remote sensing techniques based on machine learning algorithms. Satellite images, including Landsat-8 and Sentinel-2, are employed as data for the study. Additionally, the study utilizes the Support Vector Machine (SVM) algorithm to classify land use/land cover and monitor their changes during the period from 2013 to 2023. The results of this study show that the built-up area expanded in different directions during each period. Especially in the last 5-year period, the built-up area expanded rapidly, continuously, and mainly to the East of the centre of Hanoi, while in the previous period, it expanded much to the West. In the past 10 years, the built-up area has increased by approximately 11.56 square kilometres, of which 5.36 and 6.20 square kilometres were increased in the period 2013–2018 and 2018–2023, respectively. The results effectively contribute to urban planning and management, monitoring of environmental protection management, and sustainable development.