Surface albedo is a key determinant of urban heat islands, which modulates the amount of solar energy absorbed or reflected by urban surfaces, influencing microclimate and thermal comfort. However, high-resolution albedo is usually not available, which makes the understanding of the urban thermal environment at hyperlocal difficult. This study presents the first high-resolution urban albedo maps for 34 major U.S. cities using advanced deep learning models and multisource remote sensing data. By differentiating between impervious and pervious surfaces using a combination of NAIP imagery, roof albedo data, building footprints, land cover classifications, and Sentinel-2 imagery, this work achieves sub-meter resolution in albedo mapping. Employing U-Net for impervious surface classification along with impervious (ISA) and pervious surface albedo (PSA) prediction, these models were validated in selected cities, with ISA showing an R² of 0.9028 and MAE of 0.0057, and PSA demonstrating an R² of 0.9538 and MAE of 0.0027, highlighting the precision and reliability. The datasets, made publicly available, offer essential insights for urban planning and environmental monitoring.