Reconstructing Urban Road Traffic Noise Sources Using High-Resolution Satellite Imagery and Vehicle-Based Traffic Activity Estimation

Urban-scale road traffic noise mapping is frequently constrained by limited access to spatially continuous traffic observations, particularly in data-scarce cities. This study developed an integrated framework for reconstructing road-segment traffic activity from high-resolution satellite imagery and applying it to physical noise modeling in central Changshu, China. Vehicles were detected using YOLOv7, converted to centroid points, and assigned to road segments through class-specific spatial buffers. Snapshot vehicle density was combined with road-class-specific characteristic speeds to estimate flow-equivalent traffic activity, which was incorporated with road-network and building data into OpeNoise Map. Model outputs were evaluated against measured equivalent sound levels at 88 monitoring sites. Road-class statistics were calculated for both all eligible segments, including zero detections, and positive-detection segments only. Arterial roads exhibited the highest mean activity under both definitions, followed by expressways, while including zero-detection segments substantially reduced mean and median estimates, particularly for secondary roads. Predicted noise showed a pronounced road-dependent pattern, with high levels concentrated along expressways, arterial roads, and major intersections and decreasing toward interior urban blocks. Validation produced a mean bias of 1.29 dB, mean absolute error of 5.46 dB, root-mean-square error of 7.81 dB, Pearson correlation coefficient of 0.497, and R2 of 0.247. The small bias indicates limited overall systematic deviation, whereas the modest correlation and R2 demonstrate limited reproduction of site-level variability. The framework is therefore more suitable for reconstructing broad urban noise patterns, identifying potential high-noise corridors, and supporting preliminary spatial screening than for precise site-level prediction. It represents image–time conditions and should complement continuous traffic monitoring and statutory acoustic assessment, with calibration using multi-temporal imagery and synchronized ground observations.