The support of coexisting Ultra-Reliable and Low-Latency Communication (URLLC) and enhanced Mobile Broad-Band (eMBB) services is a cornerstone challenge to wireless communication networks. Those two types of services introduce strict resource allocation requirements resulting in a power-struggle between reliability, latency, and resource utilization. The difficulty in addressing that challenge could be rooted in the predominant reactive approach to resource allocation in wireless networks, where the allocation operation is carried out based on received service requests and global network statistics. This paper proposes a novel framework termed service identification to develop proactive resource allocation algorithms. The framework is based on visual data and deep learning, and its objective is to equip future wireless networks with the ability to anticipate incoming services and perform proactive resource allocation. To demonstrate the potential of the framework, a wireless network scenario with two coexisting URLLC and eMBB services is considered, and a deep learning algorithm is designed to utilize RGB video frames and predict incoming service type and its request time. An evaluation dataset is developed and used to evaluate the performance of the algorithm. The results show that the algorithm enables a 78% utilization of idle network resources, which emphasizes the value of proaction.