OSMGen: Highly Controllable Satellite Image Synthesis using OpenStreetMap Data

Amir Ziashahabi, Narges Ghasemi, Sajjad Shahabi, John Krumm, Salman Avestimehr, Cyrus Shahabi

Keywords: Geospatial AI, Diffusion Models, Urban Planning, Change Detection

TL;DR: This paper introduces OSMGen, a generative framework that synthesizes highly controllable satellite images from structured OpenStreetMap data and enables the creation of consistent before-after pairs for data augmentation and urban simulation

Abstract:

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.

Submission Number: 71