Millimeter-wave (mmWave) networks, the core technology of 5G-and-beyond, offer substantially higher data rates than traditional wireless networks, but the communications are limited to Line-Of-Sight (LOS) and very few reflection paths. So, the network relies on short-range base-stations called “picocells.” Since the paths are prone to obstructions and specular reflections, networks require careful picocell placement. Furthermore, picocells must be densely deployed to compensate for their short-range, and often demand unintuitive placement locations to maximize their effectiveness. Because of the placement density and accuracy requirements, thorough site surveys are often time consuming and expensive. To this end, we have proposed, designed, and validated a low-cost, visual data and deep learning based approaches to predict the mmWave reflections profiles indoors and outdoors, which, in turn, maximize the capacity of the networks by identifying optimal picocell placements. My team is continuing this work to design scalable and real-time systems that can facilitate fast deployment of the NextG network architecture.