But, could this "model" be used for something like monitoring land use in a city? The specific example I'm thinking of is getting a percentage breakdown of what land devoted to paved surfaces (parking/roads), to vacant undeveloped lots, and to built structures. It would also be interesting to see how those percentages have changed over time.
That may not be fine enough resolution in the source data to resolve parking lots Vs undeveloped areas to the degree you require.
The alternatives are to use the nature of the models (trained to lock in on multispectral signatures) on finer source data - which may limit your options about the globe, OR
to use urban data from city land agencies which have maps from low level air photo surveys with resolution down to 10 cm (+/-) and GIS land boundaries which are often classified via metadata.
Air photos are not multispectral (usually) so you won't have access to IR bands etc.
You can get coarse city growth figures globally from sat data going back to TERRA (launched 1999 IIRC) and fine grained air photo data from well off cities going back to (say) the mid 80s (and longer for wet negatives).
The typical trick is to look for areas which absorb visible red while reflect near infrared to identify vegetation. If all you have is rgb imagery then you can use machine learning techniques to develop a classification system.
It does not look like this model means a breakthrough in your application area. Definietly not out of the box, maybe with more work you can refine it to do the classification you are looking for.
Do you have access to satelite images of areas where you would be interested in these percentages?
Answering my own question: it seems one can access the right type of data from the Sentinel satelites relatively freely.