BuildingHeights
From open USGS lidar
to open Overture maps

SummaryAs part of the initial Overture Maps Foundation data release, we are publishing heights forover six million buildings estimated using over 34,000 km2 of open 3D Elevation Program(3DEP) lidar data from the US Geological Survey (USGS) — enabling GIS analysis and richer
map visualizations. The estimated heights apply both to buildings in OpenStreetMap (OSM)
as well as to buildings detected from imagery by Microsoft. This is the initial installmentof estimated building heights; over the next several months, we plan to release estimatedheights for the majority of US buildings in OpenStreetMap. In addition to increased coverage, we will also continue to refine our process in order to increase the accuracy of ourestimations.

Boston without building heights

Boston with building heights
MAP DATA FROM OPENSTREETMAP

Many map visualizations already make use of building heights. By increasing the percentage of open buildingheights in the US, we can enable a richer map experience, whether through better visualizations or throughimproved infrastructure for Visual Positioning and other advanced technology.

Motivation
Per-building height estimates can enrich GIS analysis and enhance map visualization. Whilethe USGS has made elevation data available both as point clouds and as digital elevation
rasters, having the height attached to a particular building greatly simplifies applications
and analyses. Currently in OpenStreetMap, approximately 10% of the buildings in the US aretagged with estimated heights or number of levels. In the past, some of these height tags
have been populated by local mappers and GIS departments. By using open 3DEP data, wehave an opportunity to populate them at scale.

BUILDING HEIGHTS: FROM OPEN USGS LIDAR TO OPEN OVERTURE MAPS

2Scale of coverage
For this initial release, we have focused on a small set of pilot areas. Over the course of theyear, we plan to continually increase our coverage over the entire contiguous US. This release includes building heights in:▶ Eastern Massachusetts (Boston)▶ Orange County, FL (Orlando)▶ Cook County, IL (Chicago)▶ Maricopa and Pinal Counties, AZ (Phoenix)▶ King County, WA (Seattle)▶ Santa Clara County, CA (San Jose)The goal of the 3DEP program is to acquire lidar across the nation and “provide the first-ever national baseline of elevation data.” That open lidar data is the key input to our building
estimation pipeline and determines the areas that we can process. The map below shows thescale of current 3DEP lidar available from the USGS color coded by quality level (QL).

This initial release focused on the 3DEP data with highest quality level (QL1 in dark green), but we plan to­process QL1 and QL2 regions (light green) covering the majority of OSM buildings in the US.

BUILDING HEIGHTS: FROM OPEN USGS LIDAR TO OPEN OVERTURE MAPS

3Height estimation

MAP DATA FROM OPENSTREETMAP

To estimate the height of a building, we use the building footprint(from OSM or from imagery detections) and select lidar samples
within that footprint (See figure).
We then calculate statistics on
those lidar samples to provide anestimate of the absolute elevation of the tallest part of thebuilding. The lidar is an excellent
source to measure this elevation
due to its high accuracy; the QL1and QL2 data is specified to havevertical error not exceeding 10cm
RMSE. However, instead of elevation, we want to provide a building’s height above the ground. To
do this we need to subtract theelevation of the ground. We currently sample bare earth terrain
sources (including 1m USGS digital terrain models and/or Mapzen
terrain tiles) along the building’sperimeter.

We estimate height by sampling LiDAR points within building
footprints.

Quality assurance
In order to validate the estimations, analysts inspect a sample of buildings using aerialimagery. With an internal tool, they select the building’s highest point from multiple views
and compute an absolute elevation of the building by triangulation. This alerts us to quality issues and gives us confidence in the performance of the algorithm. Quality assurance(QA) is currently performed on each lidar project (a project is a USGS region collected at thesame time and under the same conditions) by taking random building samples, stratified
across urban density. During the runup to this initial release, manual checks on more than
3,000 buildings were performed.

BUILDING HEIGHTS: FROM OPEN USGS LIDAR TO OPEN OVERTURE MAPS

4

Technical challenges and future improvements
Any automated process is going to produce some errors. A large part of our effort has been
to catch these errors and improve incrementally. Further improvements will be reflected infuture releases. Technical challenges and planned improvements include:▶ Alternative bare earth terrain sources. Where available, we are using 1m bare earth
terrain from the USGS. This is a flagship product of the 3DEP program and is extremelyreliable. However, in some areas where this is not available, we currently rely on Mapzen
terrain tiles. While globally complete, these offer lower spatial resolution and can occasionally produce spurious values which, when subtracted from roof elevation, directly
affect the height estimate. In future releases, we plan to use the point cloud to estimate
the elevation of the terrain instead.
▶ Point clouds without building classifications. For some USGS projects, the vendor hasclassified building points in the point clouds. In these cases, height estimation is verystraightforward since we can restrict our statistics to these points. However in about 85%of projects, building points are not classified separately. We notice this especially when atree overhangs the footprint of a building. Since these points are included in the statistics,
that building’s height will be overestimated. In future releases we will continue to refine
our approach including a combination of alternative estimators, taking into account thespatial distribution of the points, using last return lidar points, performing our own pointclassification, and possibly using coregistered imagery to segment trees.
▶ Decreased point density. This current release prioritized using lidar of QL1 – the mostdense point clouds. As we scale to using less dense QL2 lidar, we may have to discard
smaller buildings. It is also possible that we will undersample small rooftop structures likespires and thus underestimate the maximum building height.
▶ Misalignment between building footprints and lidar. Since the building footprints andthe lidar point cloud are collected separately, they can sometimes be misaligned. Whenthe scale of the misalignment is on the order of the distance between buildings, it is possible that samples from neighboring buildings can contaminate the sample of a heightmeasurement. Here again, we are considering more sophisticated estimators that use thespatial distribution of the points.
▶ New buildings. Similarly, there are cases when we have a new building footprint, but thebuilding did not exist at the time of the lidar collection. In these circumstances we willoften omit any estimation, but it is possible that old, high vegetation points in the footprint could be considered. Point classification improvements will help here, too.

BUILDING HEIGHTS: FROM OPEN USGS LIDAR TO OPEN OVERTURE MAPS

5Open data, open maps
The Overture Maps Foundation intends to use collaborative map building to generatehigh quality open map data. This project is an example of the benefits of this type of collaboration. It is the result of community contributions, industry partnerships, and government-supported open data. No single party in this collaboration could have achieved thisoutcome by itself, but by combining data, technology and engineering, new open map
datasets can be built. We hope in the future to spur similar innovation and collaboration on
all types of map data.