GreenViT: A Vision Transformer with Single-Path Progressive Upsampling for Urban Green-Space Segmentation and Auditable Area Estimation

Urban green-space monitoring in dense cityscapes remains limited by accuracy–efficiency trade-offs and the absence of integrated, auditable area estimation. We introduce GreenViT, a Vision Transformer (ViT) based framework for precise segmentation and transparent quantification of urban green space. GreenViT couples a ViT-L/14 backbone with a lightweight single-path, progressive upsampling decoder (Green Head), preserving global context while recovering thin structures. Experiments were conducted on a manually annotated dataset of 20 high-resolution satellite images collected from Satellites.Pro, covering five land-cover classes (background, green space, building, road, and water). Using a 224 × 224 sliding window sampling scheme, the 20 images yield 62,650 training/validation patches. Under five-fold evaluation, it attains 0.9200 ± 0.0243 mIoU, 0.9580 ± 0.0135 Dice, and 0.9570 PA, and the calibrated estimator achieves 1.10% relative area error. Overall, GreenViT strikes a strong balance between accuracy and efficiency, making it particularly well-suited for thin or boundary-rich classes. It can be used to support planning evaluations, green-space statistics, urban renewal assessments, and ecological red-line verification, while providing reliable green-area metrics to support urban heat mitigation and pollution control efforts. This makes it highly suitable for decision-oriented long-term monitoring and management assessments.