Love it. Vaguely related and potentially boring ramble, from the perspective of a dinosaur/former geospatial tech : starting in the 2000's, we went through the

Love it. Vaguely related and potentially boring ramble, from the perspective of a dinosaur/former geospatial tech : starting in the 2000's, we went through the emergence of the trend where local governments commissioned (part-)district-wide LiDAR fly-overs, and even better, were willing to release the resulting digital data to the public. Even over a decade ago, we private sector techs were receiving those city-wide LiDAR datasets en-masse, for our everyday usage in small scale analysis, maps and plans. The data sharing policies of local governments did (and probably still do) vary widely between different areas, but sometimes, the LiDAR data would be passed on in the form of multiple layers : "ground terrain", "buildings", "tree canopy", as initially captured and computed by the LiDAR operators by way of varying frequencies. We office techs would figure out and run the procedures necessary to adapt the data to any given request. We usually used commercial software and routines to create small scale product, and analysis of this precise nature - sun/shade, and viewshed (or "what a viewer will see, from a given point"), were a couple of services that were called upon, even back then. The early seeds of inclusion of tree canopy LiDAR specifically in such work, at a small neighbourhood scale, began to percolate through the private sector (yes, the seeds percolated, thank you metaphor gods), but it was new and rare, and to see this work now at large scale is heartening. While my dinosaur experience includes very little regarding the serving of large scale data online, dealing with larger scale raw geospatial datasets has always been one of, if not the, key challenges of working in this area. All of the work boils down to the translation or abstraction of those sources, into efficient, easily digestible and suitably focused outputs. In that sense, the CS, and more specifically data science, aspects of this type of work are what really drive the practical implementation of such innovations at large scale.