Title: Correcting rural building annotations in OpenStreetMap using convolutional neural networks,
Abstract: Rural building mapping is paramount to support demographic studies and plan
actions in response to crisis that affect those areas. Rural building
annotations exist in OpenStreetMap (OSM), but their quality and quantity are
not sufficient for training models that can create accurate rural building
maps. The problems with these annotations essentially fall into three
categories: (i) most commonly, many annotations are geometrically misaligned
with the updated imagery; (ii) some annotations do not correspond to buildings
in the images (they are misannotations or the buildings have been destroyed);
and (iii) some annotations are missing for buildings in the images (the
buildings were never annotated or were built between subsequent image
acquisitions). First, we propose a method based on Markov Random Field (MRF) to
align the buildings with their annotations. The method maximizes the
correlation between annotations and a building probability map while enforcing
that nearby buildings have similar alignment vectors. Second, the annotations
with no evidence in the building probability map are removed. Third, we present
a method to detect non-annotated buildings with predefined shapes and add their
annotation. The proposed methodology shows considerable improvement in accuracy
of the OSM annotations for two regions of Tanzania and Zimbabwe, being more
accurate than state-of-the-art baselines.