Mountain roads are vital infrastructure for transportation，logistics，and socioeconomic development in remote，topographically challenging regions. They provide essential connectivity for education，healthcare，resource transport，and disaster relief，yet their construction and maintenance are extremely difficult because of steep slopes，unstable geology，and frequent natural hazards. Despite their importance，automatically extracting mountain roads from high-resolution remote-sensing imagery remains a longstanding challenge. Complex terrain introduces severe occlusion and illumination variation，while the spectral appearance of roads is easily confused with bare rock，dry riverbeds，and agricultural paths. In addition，mountain roads are typically narrow，winding，and discontinuous，which differs substantially from the wide，regular patterns found in urban or rural road networks. Existing public road-extraction datasets，such as SpaceNet and DeepGlobe，were developed primarily for flat or urbanized environments. As a result，they fail to capture the spectral，geometric，and contextual complexity of mountain roads，leading to poor generalization when deep-learning models are transferred to high-relief regions. To bridge this gap，we construct AsiaMountain-Road，the first high-resolution，multi-country benchmark dataset dedicated to mountain-road extraction，and propose a novel multi-resolution adaptive feature dual aggregation network （MR-AFDANet） to evaluate and enhance model adaptability in diverse mountainous environments.

The AsiaMountain-Road dataset was built from 29 high-resolution satellite-image areas selected from representative mountainous regions in 17 Asian countries，covering a total of 1 836 km² and providing detailed surface information for fine-scale mapping. All imagery underwent radiometric calibration，orthorectification，and mosaicking，followed by manual annotation by trained experts. Each region was labeled with fine-grained binary road masks to ensure pixel-level accuracy. To improve usability，auxiliary metadata，including elevation maps and land-cover references，were also provided. For algorithmic evaluation，we benchmark several mainstream deep-learning models—D-LinkNet，OareNet，C2Net，AFDANet，RCFSNet，and RS-Mamba—to assess their adaptability to mountainous terrain. Building on these findings，we propose MR-AFDANet，a model specifically designed for complex terrain mapping. MR-AFDANet uses a ResNet34 encoder as its backbone，consisting of four downsampling convolutional blocks. Each block is followed by a SEAF （spatial-enhanced adaptive feature fusion） module，which adaptively fuses intra-block and residual features while strengthening spatial information through multi-scale convolutions and attention mechanisms. In the decoder，an strip refinement attention decoder （SRADecoder） progressively fuses multi-resolution features to refine road boundaries，preserve continuity in narrow and winding segments，and suppress background noise caused by shadows and vegetation. This design enables the network to preserve thin road topology even under severe spectral interference.

Experimental evaluation on the AsiaMountain-Road benchmark demonstrates the substantial difficulty of road extraction in mountainous regions. Compared with urban or plain datasets，mountain imagery contains more complex topography，stronger illumination variation，and more severe occlusion from vegetation and terrain shadows，all of which significantly degrade segmentation accuracy. Across six state-of-the-art baselines，all models show varying degrees of road omission and fragmentation，particularly on narrow，winding，and shaded mountain roads. The results further show that models trained only on source-domain data struggle to generalize to the mountainous target domain because of large distributional discrepancies. Quantitative comparisons indicate that MR-AFDANet achieves the best overall performance，obtaining the highest intersection over union（IoU），F1-score，and recall among all evaluated methods. These improvements confirm that combining multi-scale feature fusion with the SRADecoder enhances both global consistency and fine-grained boundary precision. Visual analysis further verifies this advantage： MR-AFDANet produces more continuous and complete road extractions under severe occlusion，maintains connectivity along steep and curved routes，and reduces false detections on spectrally similar surfaces.

We present AsiaMountain-Road，the first high-resolution，multi-country dataset dedicated to road extraction in mountainous regions. Covering 29 representative areas across 17 Asian countries，it provides detailed road-mask annotations together with auxiliary contextual and topographic data，filling an important gap in current remote-sensing resources. Baseline experiments show that existing state-of-the-art methods struggle with narrow，winding，and occluded roads in complex terrain. To address these challenges，MR-AFDANet integrates SEAF modules，a dual-resolution feature-fusion mechanism，and the SRADecoder to recover thin road structures more precisely and improve robustness across highly variable mountain landscapes. Overall，AsiaMountain-Road narrows the gap between real-world geospatial challenges and current computer-vision capabilities by offering both a demanding benchmark for algorithmic innovation and a valuable resource for applied geospatial analysis. The dataset and code associated with this paper are available at https://cstr.cn/31253.11.sciencedb.j00240.00109 and https://github.com/wyl-ucas/AsianMountain-DataSet.