Accurate, detailed urban forest mapping contributes to ecological status monitoring and formulating sustainable development policies in cities worldwide. However, accurate urban forest identification in southern Chinese cities is challenging when samples are insufficient because of high fragmentation and the influence of mountain shadows and cloudy weather. Therefore, this study combined the advantages of transfer, deep, and ensemble learning to propose a VGG16-UNet++&Stacking algorithm for urban forest mapping in heavily urbanized areas based on the Sentinel dataset. Initially, the algorithm ...
