Science

Using machine learning to build safer cities

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 29/10/2026, 23:55:26 UTC.

Briefings

A 2026 study published in Scientific Reports analyzed satellite data and machine learning models to identify landslide-prone zones in eThekwini, South Africa. The research found that urban development activities, specifically road construction, property development, and vegetation loss, significantly increase landslide susceptibility in the region.

Key points

• Researchers in eThekwini, South Africa, used machine learning and satellite imagery to map landslide-prone areas.
• Urban development activities like road construction and vegetation loss significantly increase landslide susceptibility.

Context

This research addresses climate change impacts on South African infrastructure, providing data to inform land-use planning and disaster risk reduction strategies in developing urban areas.

Limitations

The study covers only eThekwini, South Africa; it does not provide nationwide statistics or historical trends. Results are based on a single snapshot of 2026 data and may not reflect long-term patterns.

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