The data models for any non-trivial sensor analysis are intrinsically spatiotemporal -- every measurement or event happens at a place and time. Spatial relationships are central to the proposition of analytically reconstructing the dynamics of the physical world from disparate entities and sensors. The objective is to sample enough pixels and their relationships to sketch an accurate picture of reality as it happens. For example, a car is trying to understand its relationship to every relevant static and dynamic entity in its environment that affects its ability to operate safely. There is no business that does not benefit from having a model of reality that converges on ground truth in real-time, if you can take advantage of it.

Most of the analysis that is done usually falls under one of two categories. First, inferring (you can rarely measure it directly) when something has changed in the real world that is relevant to your business so that you can adapt to it immediately -- the applies to everything from autonomous driving to agricultural supply chains. Second, detecting anomalies -- the unknown unknowns -- so that risks can be managed when the real world appears to not conform to the models upon which you base decisions. A third category is support of industrial automation, which benefits immensely from high-resolution multimodal sensor data models, though this is largely a cost reduction measure. These categories are hand-wavy but in practice, boring industrial companies have concrete metrics they are trying to achieve or risks they are trying to manage in the most efficient way possible.