Large Scale Deep Learning for Map Making
Europa room - 3rd floor
20th November, 12:00-12:30
In this presentation, we will talk about what we have learned from our experience building a horizontally scalable deep learning pipeline that can process the > 120 million images in the OpenStreetCam database. We will offer a technical perspective about how we used state of the art algorithms to detect a large number of traffic signs, cluster those detections together and finally extract the real-life GPS locations of the detected signs. This technology is now open source and available for anyone to use and improve upon
I am very passionate about everything related to AI & Machine Learning. As I trully believe that these technologies will massively impact and change the society that we live in, I am always eager to learn about new AI frameworks, investigate interesting datasets and further my knowledge in the field. Currently, I work at Telenav, being one of the engineers in the Computer Vision team, where I help develop our AI platform in order to extract useful features from images in order to improve our maps.