Winter accessibility poses critical challenges in cold-climate cities such as Qu&eacute;bec City, where snow and ice accumulation restrict the mobility of people with disabilities. This study presents an AI-driven multimodal framework designed to detect, classify, and map winter barriers affecting pedestrian accessibility in Qu&eacute;bec. Building upon the SNOWMAN project, synthetic image and textual datasets were developed to represent seven major snow- and ice-related obstacle categories, including icy ruts, deep snow, and uncleared sidewalks. The visual modality employed a self-supervised SimCLR model for snow-barrier classification (<em>F</em><span style="font-size: 10.5px;">1</span>-score = 0.93), while the textual modality used a fine-tuned BERT classifier, achieving an F1-score of 1.00 on the synthetic test set. Canonical Correlation Analysis (CCA) aligned the two modalities into a shared latent space, enabling spatial fusion of visual and semantic embeddings for integrated analysis within the MobiliSIG Winter Mobility platform. The fused data produced dynamic accessibility maps revealing clusters of recurring winter hazards in known high-risk zones. The results confirm the feasibility of using synthetic multimodal data to simulate pedestrian-scale winter conditions and demonstrate the potential of multimodal AI for inclusive, data-driven mobility management in cold-climate cities.