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This paper tackles the task of estimating the topology of road networks fromaerial images. Building on top of a global model that performs a densesemantical classification of the pixels of the image, we design a ConvolutionalNeural Network (CNN) that predicts the local connectivity among the centralpixel of an input patch and its border points. By iterating this localconnectivity we sweep the whole image and infer the global topology of the roadnetwork, inspired by a human delineating a complex network with the tip oftheir finger. We perform an extensive and comprehensive qualitative andquantitative evaluation on the road network estimation task, and show that ourmethod also generalizes well when moving to networks of retinal vessels.

Iterative Deep Learning for Road Topology Extraction