Researchers at the UC San Diego Big Pixel Initiative are using unique tools to map urban areas, potentially revolutionizing large-scale analysis of urbanization. Using Google Earth Engine, they developed and tested new machine-learning approaches that use high-resolution satellite data to detect and map settlements around the globe.
These methods will eventually allow for the creation of a high-resolution map of all inhabited locations and for a better understanding of how cities expand and evolve. They provide, for the first time, a reliable and comprehensive open-source data for detecting and mapping urban areas through satellite images.
Urbanization is a fundamental force that shapes almost all dimensions of the modern world, from land cover and land use around cities to economics and policy making. However, the rate and magnitude of these changes have not yet been mapped globally with sharp precision.
In response, the research team constructed a unique dataset of manually classified image samples representing different forms of built-up and not built-up land cover in India. Their goal in part is to use high-resolution satellite-data to create a continuous map of the urbanization process: for the first time looking extensively over time and over large-scale areas.
Although the initial research was designed to detect urban areas in India, the methodology can easily be applied to other countries and regions, and will have impacts for governments, policy makers, business and property development as well as humanitarian and environmental workers.
Founded by Gordon Hanson of the School of Global Policy and Strategy, and Albert Yu-Min Lin of the Qualcomm Institute, the Big Pixel Initiative’s mission is to develop advanced geospatial capacity to address the world’s greatest challenges. This research in particular was funded in part by the School of Global Policy and Strategy’s Center on Global Transformation.