Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image

• URL: http://arxiv.org/abs/2606.24138v1
• Date: Tue, 23 Jun 2026 04:46:04 GMT
• Title: Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image
• Abstract summary: Sat2City v2 is a framework for creating explicit 3D rendering of city assets from a single satellite image.<n>We build a framework enabling the best scale overall performance among Sat2City v2 benchmarks.<n>Overall, our task achieves 3D rendering to explicit textured mesh assets to the best of our knowledge.
• Score: 39.47225879331284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating explicit 3D city assets from a single satellite image is important for digital twins, urban simulation, and geospatial intelligence. Unlike satellite-to-street-view synthesis, the task requires a reusable textured mesh with plausible geometry and controllable appearance rather than a 3D proxy optimized only for rendering a small set of images or videos. The ICCV Sat2City framework made a first step by conditioning cascaded sparse-voxel latent diffusion on satellite-derived height maps, but its appearance was random, its training data were synthetic, and its task-specific VAE did not scale well to noisy real-world reconstructions. We present Sat2City v2, a journal extension that adapts a pretrained native structured-latent 3D foundation model to weakly aligned satellite images and textured meshes. We build a real-world dataset with 16,241 satellite-mesh pairs across 24 regions in 9 cities. Instead of learning a 3D representation from noisy city meshes, Sat2City v2 encodes each mesh into a pretrained native 3D latent space, fine-tunes a satellite-conditioned geometry flow, and uses the decoded shape to anchor satellite-conditioned texturing. This retains Sat2City's geometry-to-appearance cascade while enabling appearance-controllable generation from the satellite input. Experiments on metric-scale DSM reconstruction and generative city-asset benchmarks for geometry and appearance show that Sat2City v2 achieves the best overall performance among evaluated baselines. Overall, Sat2City v2 advances satellite-to-city generation from rendering-oriented 3D proxies to explicit textured mesh assets, supported by, to the best of our knowledge, the first documented satellite-mesh paired dataset collected from matched geographic crops for this asset-level task. Project page: https://ai4city-hkust.github.io/Sat2City-v2/

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