BEAM: A Machine Learning Tool for Building Footprint Extraction in eThekwini Municipality, South Africa

One-quarter of the world’s urban population resides in informal settlements, a figure projected to grow in coming years. These settlements house approximately 1 billion people who often face precarious living conditions, including substandard housing, insecure land tenure, and limited access to essential services like clean water, electricity, and sanitation. While improving these conditions remains an urgent global priority, rapid urban growth has led to outdated and unreliable data on these areas, hindering effective urban planning and service delivery. To address this challenge, the United Nations Innovation Technology Accelerator for Cities (UNITAC Hamburg) developed the Building and Establishment Automated Mapper (BEAM). This machine learning tool uses aerial imagery to map informal structures and was tested in partnership with the Human Settlement Unit of eThekwini Municipality in South Africa. This research examines the challenges of mapping informal settlements through the lens of BEAM’s development and implementation in eThekwini.