It's machine-learning generated "slop" honestly.

Looking at where I live and where I grew up the building heights are quite badly estimated.

- Some groups of houses around here that are more or less identically built but on sloped terrain are reported to have widly differing heights

- My neighbour building is reported to be half the height of this building (they're more or less equally high at 5 stories)

- A small office shack behind the neighbour building is reported to be taller than it (it's a single-story building, the neighbour building is 5 stories)

- The freestanding buildings on the farm where I grew up are like you said, badly combined, much of the estimation there seems to be dependent on shadows,etc.

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f4c39012 3 days ago | parent | next [-]

Or, they subtracted a digital elevation model from a digital surface model, ran a point-in-polygon match against an existing building dataset, and labelled the difference as the height of the building. No ML needed.

	▲	basscomm 3 days ago \| parent \| next [-]
		There's a notice in the bottom-left corner on desktop that says: "This is a machine-learning-derived product. Errors may occur"
	▲	kjkjadksj 2 days ago \| parent \| prev [-]
		Other comments said they fed 2d aerial imagery into transformer and thats it.

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JoeAltmaier 3 days ago | parent | prev [-]

Still useful for a sense of building density. If almost everything called out is some sort of construction, then the density map of the world is a realistic estimate of human occupation.