RoadNeXt-CSS: ConvNeXt-Adapted Encoder-Decoder Road Extraction for Robust Spatial Information Systems

Background: Road extraction from remote sensing imagery is essential for geospatial information systems but faces challenges of thin structures, severe class imbalance and similarity between roads and non-roads.Objective: We propose RoadNeXt-CSS, integrating a ConvNeXt encoder, inverse frequency weighting (IFW) loss and a scale sensitive decoder to address these challenges.Methods: RoadNeXt-CSS was trained and evaluated on the Massachusetts Roads and DeepGlobe benchmarks using intersection over union (IoU) as the primary metric. ConvNeXt extracts rich spatial semantic features, IFW mitigates foreground background imbalance and the scale sensitive decoder adaptively weights multi level features. Component-wise ablation experiments were conducted to isolate the contribution of each proposed component and paired t-tests were applied to assess statistical significance against baseline models.Results: RoadNeXt-CSS achieves IoU of 0.6779 (Massachusetts Roads) and 0.6984 (DeepGlobe), outperforming RCFSNet and other baselines (p < 0.001). Ablation analysis confirms that the ConvNeXt encoder, IFW loss and scale-sensitive decoder contribute independently to performance gains.Conclusion: The three components together provide robust and statistically significant road extraction improvements, supporting transportation planning, disaster response and navigation systems.