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