Analysing Greenspace Changes in Vijayawada (2019-2024) Using Remote Sensing and Machine Learning

Urban greenspaces is a key part of the sustainable city environment, as it directly impacts ecological balance, public health, and resilience. Using Sentinel-2A satellite data processed through GEE, this study evaluates the degree to which Vijayawada, India, has changed from 2019 to 2024 by classifying land use and land cover into four categories: Greenspaces, Water, Buildup, and Others, using machine learning methods: Random Forest (RF) and Support Vector Machine (SVM). The overall greenspace patterns classify the performance and temporal dynamics of the assessment of urban vegetation trends. The results show considerable variability in the dynamics of greenspace related to the urbanization process, with RF having a higher classification accuracy over the study period. Bringing machine learning to GEE, this will usher in a scalable, efficient, and effective approach in monitoring urban greenspaces in bringing actionable insights on sustainable urban planning and environmental management.