CartoGenS Geographically ConditionedVector Scene Generation forBuilding Generalization

Building generalization across map scales requires coordinated changes in building shape, count, and boundary structure, altering both geometry and topology of the vector scene. These changes cannot be decided building by building, yet existing learning methods process individual buildings, local groups, or rasters that require vectorization. We introduce CartoGenS, an end-to-end framework that generates coarser-scale vector building scenes without predefined source-to-target building correspondences. A shared encoder represents building boundaries, road context, and source-scene physical extent. A single set of learned queries predicts vertices for the entire target scene, and directed successor prediction links them into cycles that form exterior and interior rings, so the number and nesting of output polygons are predicted rather than inherited from the source buildings. Successor scores include a learned relative-geometry term. For scene-level training and evaluation, we construct paired vector building scenes with road context for a fixed 1:10,000-to-1:25,000 transition. CartoGenS achieves the highest overall polygon IoU, panoptic quality, and boundary IoU among the evaluated methods, exceeding the strongest baseline by 9.48 percentage points in boundary IoU. Full factorial ablations highlight the importance of geographic conditioning, and relative-geometry scoring further improves overall performance.