Automated detection and classification of bike lanes using multimodal imagery

Bike lanes are a critical element of urban infrastructure that promote cycling and support sustainable transportation goals. Effective planning and evaluation require comprehensive inventory datasets that both identify the locations of bike lanes and classify their types. However, existing data collection is limited by inconsistent municipal documentation practices and resource constraints. This paper introduces a computer vision–based approach for the automated detection and classification of bike lanes using publicly available multimodal imagery. Each data sample integrates two street view images, captured from opposite directions, with a corresponding satellite image, enabling complementary perspectives. This approach allows the model to reliably detect bike lane presence and distinguish between designated (marked lanes without physical barriers) and protected (lanes separated from traffic by physical barriers) types. To optimize performance, we conduct ablation experiments across three architectural dimensions: stage of modality concatenation, fusion strategy, and label structure. We also construct a training dataset using Google Street View and satellite imagery from 28 major U.S. cities to ensure broad applicability. Applying the model to over 1000 road segments in Atlanta, Georgia, we demonstrate its scalability and accuracy in a real-world urban setting. By providing an automated, transferable method for developing bike lane inventories, this research addresses a critical gap in infrastructure documentation and supports more effective planning of bicycle networks. • Automated bike lane classification using multimodal street view and satellite imagery. • Systematic evaluation across three architectural dimensions. • Late-stage, decision-level fusion with hierarchical label yields best accuracy. • Training dataset comprises of 1800 segments from 28 U.S. cities. • Applicability validated through deployment on 1000+ road segments in Atlanta.