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The recent development in autonomous driving in-volves high-level computer vision and detailed road scene under-standing. Today, most autonomous vehicles employ expensive high quality sensor-set such as light detection and ranging (LID-AR) and HD maps with high level annotations. In this paper, we propose a scalable and affordable data collection and annotation framework, image-to-map annotation proximity (I2MAP), for af-fordance leing in autonomous driving applications. We provide a new driving dataset using our proposed framework for driving scene affordance leing by calibrating the data samples with available tags from online database such as open street map (OSM). Our benchmark consists of 40000 images with more than 40 affordance labels under various day time and weather even with very challenging heavy snow. We implemented sample ad-vanced driver-assistance systems (ADAS) functions by training our data with neural networks (NN) and cross-validate the res-ults on benchmarks like KITTI and BDD100K, which indicate the effectiveness of our framework and training models.