Semantic interpretation of dynamic visuospatial imagery calls for a general and systematic integration of methods in knowledge representation and computer vision. Towards this, we highlight research articulating & developing deep semantics, characterised by the existence of declarative models –e.g., pertaining space and motion– and corresponding formalisation and reasoning methods supporting capabilities such as semantic question-answering, relational visuospatial learning, and (non-monotonic) visuospatial explanation. We position a working model for deep semantics by highlighting select recent / closely related works from IJCAI [8, 4], AAAI [10], ILP [7], and ACS [9]. We posit that human-centred, explainable visual sensemaking necessitates both high-level semantics and low-level visual computing, with the highlighted works providing a model for systematic, modular integration of diverse multifaceted techniques developed in AI, ML, and Computer Vision.