Building Footprint Extraction in High-Density Urban Areas Based on Multi-Source Remote Sensing Data Fusion and ACM-PSPNet

Abstract

Building footprint extraction in high-density urban areas remains difficult because spectral confusion and shadows obscure building–background differences, while dense adjacency and complex boundaries hinder boundary recovery and adjacent-building separation. To address these issues, this study combines Digital Orthophoto Maps (DOM) and normalized Digital Surface Models (nDSM) and develops ACM-PSPNet for high-density built-up areas of Hong Kong. ACM-PSPNet extends the PSPNet baseline by integrating atrous spatial pyramid pooling and convolutional block attention for high-level contextual enhancement and feature recalibration, together with an MS-Fuse decoder for progressive multilevel spatial-detail recovery. The main experiments were independently repeated three times and evaluated on a spatially independent test set. In the input-source comparison, DOM+nDSM yielded a mean IoU of 84.94%, compared with 68.23% for DOM and 83.23% for nDSM alone. Using DOM+nDSM as the common input, the model comparison showed that ACM-PSPNet provided the highest performance among the eight evaluated models, with mean IoU, F1 Score, and Boundary F1 values of 87.81%, 93.13%, and 79.78%, respectively. In selected typical scenes, the under-segmentation proportion decreased from 35.7% for PSPNet to 5.0% for ACM-PSPNet. The results support ACM-PSPNet as an accuracy-oriented framework for dense urban building mapping, particularly where reliable boundary recovery and adjacent-building separation are required, although broader cross-city and cross-sensor validation remains necessary.