ASCEND: Machine Learning Enhanced Remote Sensing for Climate Monitoring and Environmental Change Detection

Cities heat unevenly because buildings, trees, roads, and open spaces affect heat, humidity, and airflow, creating microclimates that influence comfort and health. Study investigates the relationship between ambient air temperature and relative humidity to assess heat stress across diverse land-use types in Greensboro, NC using Wet-Bulb Temperature Formula. Using a mobile SMART-T sensor and a stationary HOBO logger, ground-level temperature and humidity data along chosen routes was collected during morning and evening intervals. The route spanned high-density urban zones, residential areas of varying income levels, and vegetated spaces. AI enhanced heat stress evaluation provides more accurate prediction and suggestion for city development and evaluation.