Height is the dimension every 3D city model needs and almost none of the open data actually provides. This paper documents the gap as we found it in Nairobi specifically, and the heuristic built to stand in for it while the real fix — a proper remote-sensing height dataset — waits on infrastructure that doesn't exist yet.
In plain terms: we know where 1.27 million buildings are, but for 99.7% of them nobody has ever recorded how tall they are. Rather than guess silently or ship a flat city, we estimated every missing height and attached a visible label saying how much to trust each estimate — so the map can be honest and useful at the same time.
The four tiers, drawn honestly: the fainter the building, the less we actually know about its height.
How sparse "sparse" actually is
Overture Maps' buildings layer carries height and num_floors fields wherever a contributing source (almost always OpenStreetMap, in practice) happened to tag them. Across the full 1,277,511-building Nairobi metro extract, 4,203 buildings — 0.3% — have either field populated. In a small test extract centered on the CBD, where OSM tagging density is highest anywhere in the city, the figure was still only around 1%. Outside the CBD, height data is close to absent.
This is not an OpenStreetMap failure specifically — height is a field a human mapper has to notice, measure or estimate, and manually enter, for every single building, and almost nobody does that work at scale unless there's a specific reason to (a landmark, a permit dispute, a survey). It's a structural gap in volunteer geographic data generally, not a Nairobi-specific or OSM-specific shortcoming.
The real fix, deferred on purpose
A better source exists and is specifically validated for this city: Google's Open Buildings Temporal dataset provides building presence, height, and fractional building counts as a ~4-meter-resolution raster spanning 2016–2023, licensed CC-BY/ODbL for free commercial use. Published, peer-reviewed research evaluating height-prediction accuracy across multiple cities found Open Buildings Temporal achieved a 2.5-meter mean absolute error specifically for Nairobi (mean absolute error: the average size of the mistake — here, the typical height estimate lands within 2.5 m of the true value) — accurate enough to distinguish a bungalow from a three-story building reliably, which is the resolution a usable city model actually needs.
We are not pulling that dataset yet. It requires a Google Earth Engine project tied to a real Google Cloud account, which hadn't been provisioned when this pipeline was built, and setting one up wasn't this session's job. The decision — made explicitly, not by default — was to estimate heights now with visible, honest confidence levels, and replace the lower-confidence estimates with real Open Buildings Temporal data once that access exists, rather than either blocking the entire buildings pipeline on an unrelated infrastructure dependency or shipping a height field that looks measured when it isn't.
The heuristic: four tiers, checked in order
For every building without a direct Overture height/floor value, the estimator checks, in order:
Tier H — informal settlement. If the building's centroid falls inside one of six hand-drawn approximate bounding boxes for Nairobi's major informal settlements (Kibera, Mathare, Mukuru, Korogocho, Kawangware, Dandora), height is estimated at 3.5 meters — one story. This is grounded in a well-established pattern in Nairobi urban-studies literature: informal settlement housing stock is overwhelmingly single-story, with multi-story structures the exception rather than the rule. It is explicitly not grounded in a settlement-specific height survey we don't have, and the bounding boxes themselves are approximations, not authoritative boundaries — both caveats that matter more the closer you get to a settlement's edge.
Tier m — distance and land-use. For everything else, we compute distance from the same KICC/Times Tower midpoint anchor the whole pipeline uses, bucket it into bands (8 floors within 300m of the core, 4 floors to 800m, 2 floors to 2km, 1.5 floors to 6km, 1 floor beyond), and apply a multiplier from Overture's land-use classification where one intersects the building (industrial and similar classes stay near 1.0 — tall single-volume structures, not many floors; commercial areas get a 1.2–1.3x bump; hospitals 1.4x; farmland-adjacent structures scale down). This rests on a standard, defensible premise in urban geography — building height typically decays with distance from a city's commercial core — applied here as a reasonable starting prior, not as something independently validated against real Nairobi height measurements yet.
Tier l — fallback. Where no land-use polygon intersects the building at all, the same distance-band floor count applies with no land-use multiplier. This is the least-informed tier and, as the results below show, by far the largest.
Tier M — measured, checked first, before any of the above: Overture's own height or floor-count value, converted at 3.2 meters per floor where only a floor count is given.
The distribution, reported without smoothing it over
| Confidence | Buildings | Share |
|---|---|---|
| M — measured | 4,203 | 0.3% |
| H — informal settlement | 87,459 | 6.8% |
| m — distance + land-use | 246,790 | 19.3% |
| l — fallback | 939,059 | 73.5% |
Most honest sentence in this series: for three-quarters of Nairobi's buildings, our height is a reasonable prior, not a fact — and the map says so, per building, in color.
The dominant tier is the least-informed one, by a wide margin. That's not a flattering number to publish, and it would have been easy to leave it out or bury it in a methods appendix nobody reads. We're reporting it as the headline finding instead, because the alternative — a height column that looks the same regardless of whether it's measured, well-reasoned, or a bare guess — is a worse kind of honesty failure than an unflattering distribution. The land-use layer that tiers m and l split on is itself sparse (3,782 features across roughly 1,600 square kilometers), which is the direct, traceable reason so much of the metro falls into the flat fallback: most of the city simply has no Overture land-use polygon to key off at all.
Why this is built to be replaced, not kept
Every estimate carries its confidence code alongside it in the tile data, all the way to the rendered map (/nairobi/map/, where dot color encodes confidence directly). That was a deliberate design constraint, not a nice-to-have: when real Open Buildings Temporal data eventually gets pulled, the refinement is a per-tile, per-confidence-tier replacement — swap l and m estimates for measured raster values where the raster has coverage, leave M alone, reassess H against the same raster to check whether the single-story assumption actually holds. None of that requires re-deriving footprint geometry, re-running the tiling pipeline, or touching the parts of the dataset that are already solid. The estimate was always meant to be provisional. Labeling it that way, tier by tier, is what makes the provisional part actually true instead of just claimed.
Data & methods availability
Input data and bbox as in the pipeline paper (Overture 2026-06-17.0). The estimator is the build_nairobi_tiles command in the Flux repository; the four tiers, the distance bands (8 floors ≤300 m from the KICC/Times Tower midpoint, decaying to 1 beyond 6 km, at 3.2 m per floor), the land-use multipliers, and the six settlement bounding boxes are all in that one file, so the full distribution table reproduces from public data with one command. The distance-decay premise follows the standard urban density-gradient literature (Bertaud); it has not been independently validated against measured Nairobi heights — that validation is exactly what the planned Open Buildings Temporal integration provides.
Companion papers
How the buildings themselves were obtained and tiled: WP·from-cbd-to-all-of-nairobi. Why single-source map data systematically misses the settlements this paper's H-tier covers: WP·who-is-missing-from-the-map.
