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Data relevant to flood vulnerability is minimal and infrequently collected,if at all, for much of the world. This makes it difficult to highlight areasfor humanitarian aid, monitor changes, and support communities in need. Itwould be time consuming and resource intensive to do an exhaustive study formultiple flood relevant vulnerability variables using a field survey. We use amixed methods approach to develop a survey on variables of interest and utilizean open-source crowdsourcing technique to remotely collect data with ahuman-machine interface using high-resolution satellite images and GoogleStreet View. This paper focuses on Quito, Ecuador as a case study, but themethodology can be quickly replicated to produce labelled training data inother areas. The overall project goal is to build training datasets that in thefuture will allow us to automate the mapping of flood vulnerability for urbanareas in geographic regions.

Advancing Data for Street-level Flood Vulnerability: Extraction of Variables from Google Street View in Quito, Ecuador