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This paper proposes a platform, dubbed "Translational Visual Data Platform (TVDP)", to collect, manage, analyze urban visual data which enables participating community members connected not only to enhance their individual operations but also to smartly incorporate visual data acquisition, access, analysis methods and results among them. Specifically, we focus on geo-tagged visual data since location information is essential in many smart city applications and provides a fundamental connection in managing and sharing data among collaborators. Furthermore, our study targets for an image-based machine learning platform to prepare users for the upcoming era of machine learning (ML) and artificial intelligence (AI) applications. TVDP will be used to pilot, test, and apply various visual data-intensive applications in a collaborative way. New data, methods, and extracted knowledge from one application can be effectively translated into other applications, ultimately making visual data and analysis as a smart city infrastructure. The goal is to make value creation through visual data and their analysis as broadly available as possible, thus to make social and economic problem solving more distributed and collaborative among users. This paper reports the design and implementation of TVDP in progress and partial experimental results to demonstrate its feasibility.