A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection
This work addresses the need for automated UV detection to aid city managers in policy-making, though it appears incremental as it builds on graph-based methods with a novel adaptation mechanism.
The paper tackles the problem of detecting urban villages (UVs), which are informal settlements with social risks, by proposing a contextual master-slave framework on an urban region graph to address challenges like scarce labeled data and diverse urban patterns, achieving effective detection as demonstrated through experiments in three cities.
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.