Remote-Sensing City Layout Extraction with MLLM

Zigan Zhou; Kai Li; Yupeng Deng

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arXiv · 2608.16484

Remote-Sensing City Layout Extraction with MLLM

Zigan Zhou·Kai Li·Yupeng Deng

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Abstract

Remote-sensing systems usually describe urban content with detection boxes, semantic masks, or vector boundaries. Such outputs locate classes and support image-plane scoring, yet they do not by themselves constitute an executable layout that retains object identities, typed relations, topology, and regeneration rules. Code-as-City instead casts urban-layout extraction from a single top-down image as constrained code generation with a multimodal large language model (MLLM). An image model first produces an aligned five-class semantic layout prior. Three ordered MLLM passes use the image and this prior to recover roads, land-cover regions and relations, and buildings. Deterministic normalization converts the accumulated records into a city graph and a restricted layout program. Executing the program creates a renderable 3D city layout and an orthographic semantic projection over shared geometry. The projection admits pixel-level comparison with remote-sensing masks, while named objects, relations, and editing operations remain available for synchronized regeneration of both views. Evaluated on the 100 scenes of CityLayout-100, the complete framework obtains 41.1% mean intersection-over-union and 48.3% global intersection-over-union. This result provides quantitative evidence that visual observations can be translated into inspectable, editable city code with coupled planar and 3D outputs.

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BibTeX RIS

Zigan Zhou, Kai Li, Yupeng Deng. 2026-08-17. Remote-Sensing City Layout Extraction with MLLM. https://arxiv.org/abs/2608.16484

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