In recent years, online volunteers have played important roles in disaster response. After a major disaster, hundreds of volunteers are often remotely convened by humanitarian organizations to map the affected area based on remote sensing images. Typically, the affected area is divided using a grid‐based tessellation, and each volunteer can select one grid cell to start mapping. While this approach coordinates the efforts of volunteers, it does not differentiate the priorities of different cells. As a result, volunteers may map grid cells in a random order. Due to the spatial heterogeneity, different cells may contain geographic information that is of different value to emergency responders. Ideally, cells that potentially contain more valuable information should be assigned higher priority for mapping. This article presents an analytical framework for prioritizing the mapping of cells based on the values of information contained in these cells. Our objective is to provide guidance for online volunteers so that potentially more important cells are mapped first. We present a method that is based on information value theory and focus on road networks. We apply this method to a number of simulated scenarios and to a real disaster mapping case from the 2015 Nepal earthquake.