Enhancing Urban Land Utilization Through SegFormer: A Vacant Land Analysis in Chengdu

Urban vacant land (UVL) presents significant environmental and urban planning challenges as cities expand, necessitating effective identification and management strategies. This study proposes an enhanced framework for UVL extraction, based on an improved SegFormer model, which incorporates the densely connected atrous spatial pyramid pooling module and the progressive feature pyramid network for expanded receptive field and achieve multiscale feature integration. The framework first applies a region-based stratification approach, dividing the study area into the central and expanded areas to handle varying land characteristics in different urban regions. Both pretrained and non-pretrained models were utilized to assess their effectiveness in segmentation accuracy, using high-resolution remote sensing images of Chengdu. The experimental results demonstrate the effectiveness of the framework, with the pretrained model, trained on urbanized area data from Chinese cities, achieving F1-scores of 91.34 and 90.05 and IoU values of 84.21 and 81.91 for the central and expanded areas, respectively. In contrast, the non-pretrained model yielded F1-scores of 93.08 and 92.32, with corresponding IoU values of 87.16 and 85.74. Ablation studies and robustness tests further confirm the model's stability and precision in complex application scenarios. This framework provides the accurate and efficient tool for UVL identification, contributing to improved urban land utilization and offering valuable insights for future research and urban planning.