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Existing deep-learning tools for road network generation have limited applications in flat urban areas due to their overreliance on the geometric and spatial configurations of street networks and inadequate considerations of topographic information. This paper proposes a new method of street network generation based on a generative adversarial network by designing a pre-positioned geo-extractor module and a geo-merging bypath. The two improvements employ the complementary use of geometric configurations and topographic features to automate street network generation in both flat and hilly urban areas. Our experiments demonstrate that the improved model yields a more realistic prediction of street configurations than conventional image inpainting techniques. The model’s effectiveness is further enhanced when generating streets in hilly areas. Furthermore, the geo-extractor module provides insights from the computer vision perspective in recognizing when topographic information should be considered and which topographic information should receive more attention.

A topography-aware approach to the automatic generation of urban road networks