How consistent are Earth Observation-based machine learning models for predicting poverty in sub-Saharan Africa?

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dc.creatorMlambo, Reason
dc.date2025-05
dc.date2025-05-20T08:09:15Z
dc.date2025-05-20T08:09:15Z
dc.date.accessioned2026-06-27T17:01:04Z
dc.descriptionPresented at the Jameel Observatory Community of Practice meeting and drylands food security and resilience early action research and evidence dialogue, Addis Ababa, Ethiopia, 13-16 May 2025
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/174665
dc.identifier.urihttp://hdl.handle.net/123456789/139798
dc.languageen
dc.publisherJameel Observatory for Food Security Early Action
dc.rightsOpen Access
dc.sourceMlambo, Reason. 2025. How consistent are Earth Observation-based machine learning models for predicting poverty in sub-Saharan Africa?. Poster. Presented at the Jameel Observatory Community of Practice meeting and drylands food security and resilience early action research and evidence dialogue, Addis Ababa, Ethiopia, 13-16 May 2025. Edinburgh: Jameel Observatory for Food Security Early Action
dc.subjectearth observation satellites
dc.titleHow consistent are Earth Observation-based machine learning models for predicting poverty in sub-Saharan Africa?
dc.typePoster

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Instituto Internacional de Investigaciones Pecuarias (ILRI)