Assessing climate risks from satellite imagery with machine learning: A case study of flood risks in Jakarta

Abstract

Consistent and timely assessment of climate risks is crucial for planning disaster mitigation and adaptation to climate change at the local community level. This article presents an automatized method for monitoring climate risks with machine learning on satellite imagery, specially targeting riverine and coastal floods. Our research demonstrates that disaster-related risk measurement becomes more comprehensive and multi-faceted by including the following components: hazards, exposure, and vulnerability. Our model first maps hazard-related risks with geo-spatial data, then extends the model to incorporate exposure and vulnerability. In doing so, we adopt a clustering-based supervised algorithm to sort satellite images to produce the climate risk scores at a grid-level. The developed model was tested over multiple ground-truth datasets on flood risks in the region of Jakarta, Indonesia. Results confirm that our model can assess climate risks in a granular scale and further capture potential risks in the marginalized areas (e.g., slums) that were previously hard to predict. We discuss how computational methods like ours can support humanitarian projects for developing countries.