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Along with climate change, more frequent extreme events, such as flooding andtropical cyclones, threaten the livelihoods and wellbeing of poor andvulnerable populations. One of the most immediate needs of people affected by adisaster is finding shelter. While the proliferation of data on disasters isalready helping to save lives, identifying damages in buildings, assessingshelter needs, and finding appropriate places to establish emergency sheltersor settlements require a wide range of data to be combined rapidly. To addressthis gap and make a headway in comprehensive assessments, this paper proposes amachine learning workflow that aims to fuse and rapidly analyse multimodaldata. This workflow is built around open and online data to ensure scalabilityand broad accessibility. Based on a database of 19 characteristics for morethan 200 disasters worldwide, a fusion approach at the decision level was used.This technique allows the collected multimodal data to share a common semanticspace that facilitates the prediction of individual variables. Each fusednumerical vector was fed into an unsupervised clustering algorithm calledSelf-Organizing-Maps (SOM). The trained SOM serves as a predictor for futurecases, allowing predicting consequences such as total deaths, total peopleaffected, and total damage, and provides specific recommendations forassessments in the shelter and housing sector. To achieve such prediction, asatellite image from before the disaster and the geographic and demographicconditions are shown to the trained model, which achieved a prediction accuracyof 62 %

A Machine learning approach for rapid disaster response based on multi-modal data. The case of housing & shelter needs