DeepC4: Deep Conditional Census-Constrained Clustering for Large-scale Multitask Spatial Disaggregation of Urban Morphology

A novel deep learning approach, Deep Conditional Census-Constrained Clustering (DeepC4), has been introduced to enhance large-scale spatial disaggregation of urban morphology. This method addresses challenges in mapping urban environments by utilizing satellite imagery, geospatial datasets, and census statistics, particularly in developing economies.

The implementation of DeepC4 is significant as it aims to improve the accuracy of urban morphology mapping, which is crucial for sustainable development and disaster risk reduction efforts, especially in regions like Rwanda.