ASAR-Net: A Novel Adaptive Scale-Aware Road Extraction Network for High-Resolution Remote Sensing Images

Abstract

Accurate road extraction from high-resolution remote sensing imagery plays a vital role in numerous geospatial applications, including urban planning, disaster emergency response, intelligent transportation, and map updating. However, significant variations in road width, geometry, and orientation, together with complex backgrounds such as shadows, vegetation, and occlusions, often lead to incomplete extraction and poor structural continuity. To address these challenges, this paper proposes an adaptive scale-aware road extraction network, termed ASAR-Net, which jointly improves multi-scale feature representation and structural continuity. Specifically, an Adaptive Bidirectional Enhancement Module (ABEM) is introduced in the encoder to improve the representation of roads with diverse spatial scales through adaptive scale-aware convolution and bidirectional attention. Furthermore, a Directional Fusion Module (DFM) is incorporated into the decoder to guide feature reconstruction along road orientations using dynamic snake convolution, thereby facilitating the recovery of continuous and complete road structures. Extensive experiments on two public benchmark datasets, Massachusetts Roads and DeepGlobe, demonstrate that ASAR-Net consistently outperforms several representative state-of-the-art road extraction methods in terms of mIoU and F1-score. The proposed network effectively improves both the semantic completeness and structural continuity of extracted road networks, demonstrating its robustness and effectiveness for road extraction in complex high-resolution remote sensing scenarios.