The current situation in large-scale lighting evaluation is shifting from intermittent lighting surveys measured by a lux-meter to a continually mapped and visualized lighting system which can sense, estimate, map and visualize the lighting in real-time. A survey of the relevant literature in the journals highlights research on sensing technologies, various distributed system architectures, spatial mapping techniques and visual analytics that provide the underlying infrastructure for these platforms. The studies surveyed seem to have moved from the traditional style of photosensor-based control systems to systems with multiple nodes, that use wireless proximity sensing, low cost sensor arrays that are self-calibrated, or that employ camera- and vision-based luminance mapping, to systems capable of predicting the spatial daylight or electric-light distribution from considerably fewer measurements by relying on machine-learning models. In literature, the predominant tradeoffs are accuracy, latency, energy efficiency and deployment cost, while factors such as spectral mismatch, cosine response, calibration drift, fixture feedback, network installation monitoring, and transferability of deployments between different spaces are orthogonal (not a trade-off) and still affect the deployments in the field. The next generation of systems creating a multi-sensor framework must incorporate calibrated heterogeneous sensors, uncertainty-aware spatial interpolation, image- assisted estimation, edge/cloud analytics and interoperable workflows with a digital-twin. These directions can allow for scale, real-time – lighting analysis for buildings in the office, industrial hall, atria, library and other large-scale lighting assets.