Gentrification is an urban phenomenon marked by socioeconomic shifts that can displace long-term residents and increase inequality. Accurate measurement is essential for effective urban planning and equitable development. Traditional reliance on census data is costly, slow, and lacks the spatial and temporal resolution needed to detect neighborhood-level changes in real time. This study addresses these challenges by combining open satellite imagery with machine learning techniques to quantify gentrification more effectively. By analyzing high-resolution imagery, we detect physical changes—such as shifts in building density, rooftop materials, and green spaces—that are linked to gentrification but often overlooked by census-based approaches. Using the Greater London Area as a case study, our method improves measurement accuracy by up to 8%, achieving a balanced accuracy of 77% across 4,085 neighborhoods. Even a small improvement in accuracy can enable better identification of at-risk neighborhoods, helping policymakers intervene before displacement pressures become irreversible.