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In this post, you will learn how to work with the Open Buildings 2.5D Temporal data and download it for many useful downstream applications, such as Visibility Analysis, Population Modeling, and 3D Visualization.

Google has two important large-scale AI-derived open building datasets:

  • Open Buildings V3 Polygons: This was released a few years ago and contains all buildings polygons detected from Google’s corpus of high-resolution imagery. You can read more about it in our post Mapping Building Density with Open Building Datasets.
  • Open Buildings 2.5D Temporal V1: This is a newer dataset that aims to extract useful attributes for buildings such as year of construction and building height. Since this data is derived from open-source medium-resolution Sentinel-2 imagery, it has temporal coverage from 2016-2023. A deep learning model was trained to predict building heights from Sentinel-2 images, so we also get the height information each year.

We will cover a Google Earth Engine workflow to process this data to make it usable in a GIS environment and extract a high-resolution Digital Surface Model (DSM). We will also see how to combine the Open Buildings V3 polygon building footprints with the Open Buildings 2.5D Temporal V1 data to create and extract yearly polygon datasets containing building heights that can be used in a GIS environment.

Open Buildings 2.5D Temporal Data Combined with Open Buildings V3 Polygons and Visualized in QGS

The post is divided into the following sections.

  • Part 1: Extracting Building Height Raster and High-Resolution DSM
  • Part 2: Extracting Building Footprints with Heights
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In this post, I will outline techniques for computing weighted-centroids in both QGIS and Google Earth Engine. For a polygon feature, the centroid is the geometric center. It can also be thought of as the average coordinate of all points within the polygon. There are some uses cases where you may want to compute a weighted-centroid where some parts of the polygon gets higher ‘weight’ than others. The main use-case is to calculate a population-weighted centroid. One can also use Night Lights data as a proxy for urbanized population and calculate a nightlights-weighted centroid. Some applications include:

  • Regional Planning: Locate the population-weighted centroid to know the most accessible location from the region.
  • Network Analysis: For generating demand points in location-allocation analysis, you need to convert demands from regions to points. It preferable to compute populated-weighted centroids for a more accurate analysis.

Do check out this twitter-thread by Raj Bhagat P for more discussion on weighted centroids.

Different Weighted Centroids for the State of Karnataka, India (2015)
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I recently taught a 1-month long course on GIS Applications in Urban and Regional Planning. We explored how GIS can be applied to solve problems in 6 different thematic areas. In this post, I will outline different applications and show concrete examples of using open-datasets and open-source GIS software QGIS.

Update!
The full course material – including data packages and PDF handouts – is now available for free download. Download [qgis_urban_planning.zip] containing detailed step-by-step instructions and datasets. You can also access updated versions of some tutorials at qgistutorials.com.

Here are the 6 thematic areas

  • Land Use Planning and Management
  • Crime Mapping and Analysis
  • Solid Waste Management
  • Urban Infrastructure and Utilities
  • Urban Transportation
  • Spatial Planning
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