BirdCV-LiDAR: A Multimodal Data Fusion Framework for Automated Sidewalk Infrastructure Assessment

Abstract. Assessing sidewalk infrastructure for accessibility compliance is an important task in urban planning; however, traditional methods are often manual, subjective, and resource-intensive. This paper introduces BirdCV-LiDAR, a multimodal data fusion framework for an automated assessment of sidewalk infrastructure. The proposed approach integrates high-resolution bird’s-eye-view (HR-BEV) imagery with aerial LiDAR point cloud (ALPC) data to automatically detect, measure, and assess sidewalk features for compliance with accessibility standards. By combining YOLO-oriented bounding box (OBB) models with precise LiDAR-based elevation data, the framework enables accurate dimensional and slope evaluations of sidewalk features, such as crosswalks and truncated domes. Validation with a 12-inch (0.3048 m) inclinometer shows that LiDAR-based slope measurements achieve 84.7% accuracy, with a root-mean-square error (RMSE) of 0.1152 m for crosswalk width measurements. The framework achieves 81.0% accuracy in determining ADA-PROWAG compliance, providing an adaptable, expandable solution for improved urban accessibility assessments.