This work focuses on micromapping using street-view imagery and AI techniques to enhance OpenStreetMap (OSM). Micromapping involves mapping small geographic objects and has applications in infrastructure, security, 3D modeling, and more. Despite OSM's vast data, micromapping remains limited. The proposed methodology combines AI-based semantic segmentation and monocular depth estimation to accelerate OSM data generation. The process involves selecting an image, segmenting objects, generating 3D patches, and translating them into OSM data. Testing and future work involve accuracy improvement and data integration challenges. This approach can enrich OSM, enabling diverse applications and addressing specific urban needs.