Author: Chaim Rand, Pini Reisman, and Eliyah Weinberg

In this post, we focus on one portion of the REM™ system: the automatic identification of changes to the road structure which we will refer to as Change Detection. We will share our journey of architecting and deploying a solution for Change Detection, the core of which is a deep learning model called CDNet. We will share real-life decisions and tradeoffs when building and deploying a high-scale, highly parallelized algorithmic pipeline based on a Deep Learning (DL) model, with an emphasis on efficiency and throughput.