用开放地图数据生成真实卫星影像，支持修改后实时预览变化效果。
OSMGen: Highly Controllable Satellite Image Synthesis using OpenStreetMap Data
- 直接用OSM的矢量数据生成卫星图，控制更精细。
- 可生成一致的前后对比图像对，变更只影响指定区域。
- 适合城市规划者预演改造方案，也可用于训练数据生成。
精准及时的地理空间数据对城市规划、基础设施监测和环境管理至关重要，但特定城市特征及其变化的标注数据集仍十分稀缺。我们提出OSMGen，一个从原始OpenStreetMap（OSM）数据直接生成真实卫星影像的生成框架。不同于依赖栅格瓦片的以往方法，OSMGen利用OSM JSON中完整的矢量几何、语义标签、位置与时间信息，实现对场景生成的细粒度控制。该框架的核心能力是生成一致的“前后”图像对：用户对OSM输入的编辑会转化为目标视觉变化，其余场景保持不变。这使得生成填补数据稀缺与类别不平衡的训练数据成为可能，也为规划者通过编辑地图数据直观预览方案提供了简便方式。更广泛而言，OSMGen可生成静态与变更状态的（JSON, 图像）配对数据，推动建立卫星影像自动驱动结构化OSM更新的闭环系统。源代码已开源：https://github.com/amir-zsh/OSMGen。
原文摘要 · Abstract (English)
Accurate and up-to-date geospatial data are essential for urban planning, infrastructure monitoring, and environmental management. Yet, automating urban monitoring remains difficult because curated datasets of specific urban features and their changes are scarce. We introduce OSMGen, a generative framework that creates realistic satellite imagery directly from raw OpenStreetMap (OSM) data. Unlike prior work that relies on raster tiles, OSMGen uses the full richness of OSM JSON, including vector geometries, semantic tags, location, and time, giving fine-grained control over how scenes are generated. A central feature of the framework is the ability to produce consistent before-after image pairs: user edits to OSM inputs translate into targeted visual changes, while the rest of the scene is preserved. This makes it possible to generate training data that addresses scarcity and class imbalance, and to give planners a simple way to preview proposed interventions by editing map data. More broadly, OSMGen produces paired (JSON, image) data for both static and changed states, paving the way toward a closed-loop system where satellite imagery can automatically drive structured OSM updates. Source code is available at https://github.com/amir-zsh/OSMGen.
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