The article titled "Urban Revival: Mapping urbanization impacts on surface temperature and vegetation using remote sensing and machine learning" from ESS Open Archive delves into a critical environmental issue: the effects of urban expansion on local climate. The study employs advanced remote sensing and machine learning techniques, which are well-established and scientifically validated methods for analyzing complex environmental data. This approach allows researchers to precisely map changes in land surface temperature (LST) and vegetation cover, crucial indicators of the Urban Heat Island (UHI) effect. Numerous credible sources corroborate the methodology and the general findings that urbanization typically leads to increased surface temperatures and a reduction in green spaces, exacerbating the UHI phenomenon. Studies from MDPI, Environmental Reports Journal, and various research papers highlight the effectiveness of integrating Geographic Information Systems (GIS), satellite imagery, and machine learning models to analyze urban environments and their climate impacts. This research is highly relevant globally, with particular significance for countries experiencing rapid urbanization like India, where cities such as Pune, Bengaluru, Shimla, and Dehradun have been subjects of similar studies investigating the decline in vegetation and rise in LST due to urban growth. The ESS Open Archive, as the source, is a recognized preprint server for Earth and Space Science research, facilitating the open discovery and dissemination of early research outputs. While content on ESS Open Archive is evaluated by an editorial board, it is important to note that it is typically not peer-reviewed before being posted. Therefore, while the claims and methodology are grounded in scientific principles and widely supported by other research, the specific findings of this particular paper would undergo full peer review for formal journal publication. The title itself accurately describes the study's scientific focus without sensationalism.