Abstract. In 2016, Hurricane Matthew devastated many parts of the Caribbean, in particular the country of Haiti. More than 500 people died and the damage was estimated at 1.9 billion USD. At the time, the Humanitarian OpenStreetMap Team (HOT) activated their network of volunteers to create base maps of areas affected by the hurricane, in particular coastal communities in the path of the storm. To help improve HOT’s information workflow for disaster response, one strand of the Crowd4Sat project, which was funded by the European Space Agency, focussed on examining where the Picture Pile Tool, an application for rapid image interpretation and classification, could potentially contribute. Satellite images obtained from the time that Hurricane Matthew occurred were used to simulate a situation post-event, where the aim was to demonstrate how Picture Pile could be used to create a map of building damage. The aim of this paper is to present the Picture Pile tool and show the results from this simulation, which produced a crowdsourced map of damaged buildings for a selected area of Haiti in 1 week (but with increased confidence in the results over a 3 week period). A quality assessment of the results showed that the volunteers agreed with experts and the majority of individual classifications around 92 % of the time, indicating that the crowd performed well in this task. The next stage will involve optimizing the workflow for the use of Picture Pile in future natural disaster situations.