Green infrastructure in urban settings is an essential component of city sustainability. New developments in the built environment pose a huge threat to the reduction in and health of vegetation. Monitoring urban green spaces is important to understand the extent of the change in the urban micro-climate and its impact on public health and well-being. Traditional methods, such as in situ measurements and expert observations, are often constrained by spatial and temporal limitations. The dynamic changes in urban settings need efficient planning and maintenance of green spaces. Satellite observations have become a fundamental tool to provide city-scale coverage with sound temporal coverage. Leveraging the large volume of publicly available data, advanced machine learning models could enhance our understanding and analysis of the urban environment. We explore the potential of Sentinel-2 vegetation indices such as the Normalized Difference Vegetation Index (NDVI) or the Normalized Difference Water Index (NDWI) to classify and extract useful features from urban landscapes. By utilising state-of-the-art machine learning techniques, we aim to develop a robust and scalable framework for urban environment classification. The proposed models will facilitate monitoring changes in green spaces across diverse urban contexts, enabling timely and informed decision-making to support sustainable urban development. In addition, the integration of vegetation indices contributes to actionable insights for promoting eco-friendly and sustainable urban planning while supporting the development of resilient urban ecosystems, making it a valuable tool for decision-makers and policy developers.