[H12]

This may be a silly question as I'm quite inexperienced w/ ML and a lot of the words in this article don't mean anything to me yet:

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.

[defrost]

If I skimmed the linked source and https://hls.gsfc.nasa.gov/algorithms/ correctly the models are trained on 10m x 10m ( ~ 30 ft x 30 ft ) cells.

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).

[krisoft]

The thing you are asking for does not sound hard if you have multispectral satelite data of your target area.

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?

[krisoft]

> 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.

[capableweb]

I think that'd make a lot of sense to use it for. One of the demos (https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-10...) showcases a fine-tuned version of the model that detects crop type and land usage, so hard to imagine it couldn't be done for more types that are more city-oriented.

[netrus]

If you are just interested in the percentage share (not a map of labeled data), all you need is random sampling. Classifying a couple of hundred coordinates is sufficient, takes at most some hours (more likely <1h), and is required anyway to train your network.