Remote sensing vs. field survey data in a post-earthquake context: potentialities and limits of damaged building assessment datasets

Quick building damage assessment following disasters such as large earthquakes serves to establish a preliminary estimation of losses and casualties. These datasets are completed by employing several crowdsourcing initiatives, in which volunteers and collaborators map damaged buildings in a given area at a qualitative damage scale based on a post-earthquake aerial or satellite image. Automating this process is a temptation and a technical issue, but manual interpretation remains essential, with the identification of moderate and lateral damage being the key and limiting factor. Following the Haiti 2010 earthquake, many studies were completed by crossing multilayer data gathered from different sources (satellite, aerial, and field survey). These works created a building damage dataset that enabled the construction of different sets of empirical vulnerability functions. In the present study, we proposed to review the datasets used for the damage assessment again, investigate how they can be managed for understanding urban damage patterns, and quantify the potentialities and limits of the sets.