Dilated residual U-Net with spatial–channel attention for building footprint extraction from UAV imagery in diverse landscapes

Abstract

Accurate extraction of building footprints from unmanned aerial vehicle (UAV) imagery is essential for urban planning, infrastructure monitoring, and disaster risk management. However, segmentation performance remains challenged by scale variability, spectral ambiguity, and partial occlusion in heterogeneous environments. Here, we examine the integration of dilated convolution, residual learning, and spatial–channel attention within a unified U-Net framework for building footprint extraction. Rather than introducing new architectural components, the proposed approach investigates how these established mechanisms influence segmentation behaviour across diverse landscape conditions. Multi-rate dilated convolution is incorporated in the bottleneck stage; residual connections support stable feature propagation, and attention modules enhance feature discrimination. The model was evaluated on the WHU Building Dataset and a newly developed UAV dataset (HUMG) representing diverse environmental conditions in Vietnam. Results showed improved segmentation performance relative to U-Net, Feature Pyramid Network, and SegFormer, particularly in dense urban, rural, and vegetation-occluded environments. Although the proposed model incurs higher computational costs, the additional complexity reflects both increased model capacity and integrated architectural design. The findings demonstrate that systematic adaptation of established mechanisms can enhance building segmentation robustness in high-resolution UAV imagery for offline geospatial applications.