Graph2Scene: Generating Remote Sensing Imagery and Labels via Scene Graphs and Low-Rank Representation

Abstract

High-precision, pixel-level annotations are indispensable for remote sensing semantic segmentation and related tasks, yet producing such labels manually is prohibitively expensive. Although recent generative models can synthesize realistic remote sensing data, existing approaches typically either rely heavily on preexisting ground-truth masks as conditioning inputs or lack precise control over the spatial layout of the generated content. To address this gap, we propose Graph2Scene, a novel framework for the joint generation of remote sensing images and pixel-level labels driven by scene graphs. This framework establishes a flexible control mechanism that utilizes scene graphs derived from existing semantic labels during training to learn semantic priors, while enabling users to explicitly define object quantities, categories, and topological relationships for customized generation during inference. Graph2Scene adopts a two-stage cascade: Graph2Mask encodes the scene graph into textual prompts and employs an image-level low-rank adaptation (LoRA) to finetune a FLUX model for label generation; Mask2Scene uses a class-level LoRA strategy to learn fine-grained visual features and generates remote sensing images conditioned on the label. Experiments on our developed non-agricultural conversion process (NACP) dataset and the public LoveDA dataset show that Graph2Scene effectively produces structurally coherent and visually realistic remote sensing images together with accurate pixel-level annotations. The code will be made available at https://github.com/GeoRSAI/Graph2Scene