Satellite-based Prediction of Forage Conditions for Livestock in Northern Kenya
This work addresses the severe and worsening drought exposure of pastoralists in Northern Kenya, offering a potential improvement in index-based insurance through a novel dataset and method.
This paper tackles the problem of predicting forage conditions for livestock in Northern Kenya by introducing the first satellite image dataset labeled with forage quality and applying computer vision methods, resulting in a model that significantly outperforms existing technology for drought insurance.
This paper introduces the first dataset of satellite images labeled with forage quality by on-the-ground experts and provides proof of concept for applying computer vision methods to index-based drought insurance. We also present the results of a collaborative benchmark tool used to crowdsource an accurate machine learning model on the dataset. Our methods significantly outperform the existing technology for an insurance program in Northern Kenya, suggesting that a computer vision-based approach could substantially benefit pastoralists, whose exposure to droughts is severe and worsening with climate change.