  Locating populations in rural areas of developing countries has attracted the
attention of humanitarian mapping projects since it is important to plan
actions that affect vulnerable areas. Recent efforts have tackled this problem
as the detection of buildings in aerial images. However, the quality and the
amount of rural building annotated data in open mapping services like
OpenStreetMap (OSM) is not sufficient for training accurate models for such
detection. Although these methods have the potential of aiding in the update of
rural building information, they are not accurate enough to automatically
update the rural building maps. In this paper, we explore a human-computer
interaction approach and propose an interactive method to support and optimize
the work of volunteers in OSM. The user is asked to verify/correct the
annotation of selected tiles during several iterations and therefore improving
the model with the new annotated data. The experimental results, with simulated
and real user annotation corrections, show that the proposed method greatly
reduces the amount of data that the volunteers of OSM need to verify/correct.
The proposed methodology could benefit humanitarian mapping projects, not only
by making more efficient the process of annotation but also by improving the
engagement of volunteers.
