Google Research has released the Open Buildings 2.5D Temporal Dataset, a project that tracks constructing changes across the Global South from 2016 to 2023.
This exceptionally detailed dataset offers a dynamic, year-by-year view of urbanization. It captures constructing construction, growth, and transformation across regions where such data has long been scarce or nonexistent.
John Quinn, a software engineer at Google Research, explained why the project is essential: “Not knowing where buildings are is an enormous problem for numerous practical reasons. If you’re creating services or vaccination campaigns or rescuing people after an emergency, that is a difficulty.”
The tool’s primary breakthrough is its ability to extract high-resolution insights from low-resolution Sentinel-2 satellite imagery.
By analyzing as much as 32 time-shifted images of the identical location, the AI can detect structures far smaller than a single pixel.
This effectively overcomes the restrictions of accessible imagery in lots of parts of the Global South.
Building density in 2023 based on Open Buildings 2.5D Temporal dataset data. Source: Google.
Beyond detection, the system estimates constructing heights with remarkable precision – inside 1.5 meters on average.
This transforms flat satellite images into wealthy, multi-layered data, providing urban planners and humanitarian organizations with a strong instrument for understanding population density and resource needs.
AI is transforming our approach to mapping land use globally.
A recent project by Aya Data in Ghana, for instance. demonstrates the technology’s potential. They employed AI to research 1000’s of satellite images of South America, tracking changes in forests, urban areas, and agricultural land over several years.
The AI’s skill in detecting subtle changes, comparable to early signs of deforestation or latest developments, informs real-world agriculture and conservation policies.
Beyond observing long-term shifts within the environment, AI enables researchers to detect and reply to immediate threats like illegal logging, wildlife poaching, and unauthorized changes in land use visible from satellite imagery.
Mapping the Global South
The term “Global South” typically refers to developing nations in Africa, Asia, Latin America, and the Caribbean.
These regions, home to nearly all of the world’s population, often face challenges in delivering high-quality healthcare, urban development, environmental conservation, and resource management.
Abdoulaye Diack, a program manager on the project, stated, “We want people in the worldwide South making policy decisions to have the identical tools available as the worldwide North.”
The dataset is already finding practical applications. In Uganda, the nonprofit Sunbird AI is leveraging this data for rural electrification projects.
WorldPop, based on the University of Southampton, is using it to refine global population estimates – critical information for planning every part from vaccination campaigns to disaster response.
WorldPop’s director, Professor Andrew Tatem, explained why this data is essential: “Understanding where people live is significant for ensuring that resources are distributed fairly and that nobody is left behind in delivering services like healthcare.”
Google admits that the system has limitations. Persistent cloud cover can impede data collection in a number of the world’s most humid regions, and really small structures may additionally elude detection.
While not without limitations, the Open Buildings 2.5D Temporal Dataset is a superb example of the powerful synergy between data democratization and AI.
By democratizing access to complex geospatial information, it paves the best way for more equitable urban planning, disaster preparedness, and sustainable development on a worldwide scale.
