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Mapping and Classifying Settlement Locations

Discusses GRID3’s work on collecting and analysing settlements data. GRID3’s settlements work has two areas of focus: creating a comprehensive settlement layer that enables a real-world picture of communities, and using building footprints, geospatial data layers, and machine learning algorithms to classify structures and local areas within settlements. The paper also discusses the applications of GRID3’s methods in Nigeria, the Democratic Republic of the Congo, and Zambia.

Authors Center for International Earth Science Information Network; Flowminder Foundation; United Nations Population Fund; WorldPop, University of Southampton
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GRID3 Impact Report 2017-2022

This impact report provides an overview of GRID3’s activities and successes during the program’s first phase, between 2017 and 2022. It highlights key milestones, achievements, and selected use cases from the Democratic Republic of the Congo, Nigeria, and Zambia. The […]

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The Population Seen from Space: When Satellite Images Come to the Rescue of the Census

Great steps have been made in recent decades in observing the Earth from the sky. Landscapes and infrastructure can now be mapped at an extremely fine spatial scale. These data—particularly useful to geographers—can also benefit demographers. By combining observations of […]

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Rethinking Education for Sustainable Development [Chapter 9]

This book explores how education can be used as a tool to promote sustainability practices as the world faces huge challenges related to climate change and public health. GRID3 contributed to Chapter 9, “Building Capacity for Geospatial Data-Driven Education Planning”.

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