  Urban villages (UVs) refer to the underdeveloped informal settlement falling
behind the rapid urbanization in a city. Since there are high levels of social
inequality and social risks in these UVs, it is critical for city managers to
discover all UVs for making appropriate renovation policies. Existing
approaches to detecting UVs are labor-intensive or have not fully addressed the
unique challenges in UV detection such as the scarcity of labeled UVs and the
diverse urban patterns in different regions. To this end, we first build an
urban region graph (URG) to model the urban area in a hierarchically structured
way. Then, we design a novel contextual master-slave framework to effectively
detect the urban village from the URG. The core idea of such a framework is to
firstly pre-train a basis (or master) model over the URG, and then to
adaptively derive specific (or slave) models from the basis model for different
regions. The proposed framework can learn to balance the generality and
specificity for UV detection in an urban area. Finally, we conduct extensive
experiments in three cities to demonstrate the effectiveness of our approach.

Machine Learning;Computer Vision and Pattern Recognition;Social and Information Networks