RSGen: Enhancing Layout-Driven Remote Sensing Image Generation with Diverse Edge Guidance

Xianbao Hou; Yonghao He; Zeyd Boukhers; John See; Hu Su; Wei Sui; Cong Yang

cs.CVMar 16, 2026

RSGen: Enhancing Layout-Driven Remote Sensing Image Generation with Diverse Edge Guidance

Authors: Xianbao Hou, Yonghao He, Zeyd Boukhers, John See, Hu Su, Wei Sui, Cong Yang

Organizations: School of Future Science and Engineering, Soochow University, Suzhou, China · D-Robotics, Beijing, China · Fraunhofer Institute for Applied Information Technology, Sankt Augustin, Germany · School of Mathematical and Computing Sciences, Heriot-Watt University Malaysia, Putrajaya, Malaysia ·

Abstract

Diffusion models have significantly mitigated the impact of annotated data scarcity in remote sensing (RS). Although recent approaches have successfully harnessed these models to enable diverse and controllable Layout-to-Image (L2I) synthesis, they still suffer from limited fine-grained control and fail to strictly adhere to bounding box constraints. To address these limitations, we propose RSGen, a plug-and-play framework that leverages diverse edge guidance to enhance layout-driven RS image generation. Specifically, RSGen employs a progressive enhancement strategy: 1) it first enriches the diversity of edge maps composited from retrieved training instances via Image-to-Image generation; and 2) subsequently utilizes these diverse edge maps as conditioning for existing L2I models to enforce pixel-level control within bounding boxes, ensuring the generated instances strictly adhere to the layout. Extensive experiments across three baseline models demonstrate that RSGen significantly boosts the capabilities of existing L2I models. For instance, with CC-Diff on the DOTA dataset for oriented object detection, we achieve remarkable gains of +9.8/+12.0 in YOLOScore mAP50/mAP50-95 and +1.6 in mAP on the downstream detection task. Our code will be publicly available: https://github.com/D-Robotics-AI-Lab/RSGen

Figures & tables

Figure 1: Visualization of controllability between existing L2I methods (MIGC [ 38 ] , CC-Diff [ 35 ] , FICGen [ 24 ] ) and the same methods equipped with RSGen. While the original methods (a) struggle to adhere to the specified bounding boxes, integrating our module (b) significantly enhances instance alignment and control precision.

Figure 2: Overview of RSGen, which consists of the Edge2Edge module (a) and the L2I FGControl module (b), where “CLS” denotes the class label. The Edge2Edge module enhances the diversity of retrieved edge maps through an I2I process. Subsequently, these diverse edges and layout inputs guide the L2I FGControl module, which interacts with the base L2I model to achieve precise pixel-level control. Our framework significantly increases structural diversity while ensuring fine-grained spatial alignment.

Figure 3: Visualization of diverse edge maps generated by the Edge2Edge module. Our method employs distinct random seeds to generate varied structural details within the specified bounding boxes, significantly enhancing the diversity of the structural priors.

Figure 4: Comparison of ControlNet [ 34 ] , ControlNet-XS [ 32 ] , and our FGControl. Standard global control methods suffer from feature entanglement, causing background chaos. Conversely, FGControl strictly confines high-frequency structural guidance within the layout bounding boxes, achieving fine-grained local control without interfering with the global semantic synthesis.

Method	DIOR-RSVG (HBB)			DOTA (OBB)
	FID $\downarrow$	YOLOScore $\uparrow$		FID $\downarrow$	YOLOScore $\uparrow$
	FID $\downarrow$	$\text{mAP}_{50}$	$\text{mAP}_{50-95}$	FID $\downarrow$	$\text{mAP}_{50}$	$\text{mAP}_{50-95}$
MIGC [ 38 ]	79.32	63.2	38.4	66.80	53.0	30.0
+ Ours	85.04	68.7	45.8	68.07	55.1	37.7
CC-Diff [ 35 ]	66.75	66.8	41.2	47.72	57.7	34.0
+ Ours	68.12	70.1	47.7	46.86	67.5	46.0

Table 1: Quantitative comparison of baseline models with and without our proposed RSGen on DIOR-RSVG and DOTA datasets. Models equipped with RSGen achieve substantial improvements in layout consistency (YOLOScore) alongside highly competitive generation fidelity (FID).

Method	DIOR-RSVG (HBB)			DOTA (HBB)			DOTA (OBB)
Method	mAP	$\text{mAP}_{50}$	$\text{mAP}_{75}$	mAP	$\text{mAP}_{50}$	$\text{mAP}_{75}$	$\text{mAP}_{50}$	$\text{mAP}_{75}$
MIGC [ 38 ]	54.3	78.9	59.8	38.3	64.5	39.2	53.55	19.68
+ Ours	55.0	78.7	61.1	38.9	64.5	40.5	53.19	21.92
CC-Diff [ 35 ]	54.7	78.4	60.1	37.4	63.2	38.6	54.80	20.11
+ Ours	55.0	78.8	60.7	39.0	64.2	40.4	54.92	21.65
FICGen [ 24 ]	54.5	78.7	60.7	39.2	64.8	40.8	55.81	22.66

Table 2: Comparison of downstream object detection performance. By mixing synthetic and real data at a 1:1 ratio, models trained with data generated by RSGen achieve overall accuracy improvements across both HBB and OBB settings.

Method	Ratio (Real:Syn)	DOTA (HBB)			DOTA (OBB)
Method	Ratio (Real:Syn)	mAP	$\text{mAP}_{50}$	$\text{mAP}_{75}$	$\text{mAP}_{50}$	$\text{mAP}_{75}$
CC-Diff [ 35 ]	1:3	39.0	65.2	39.8	55.72	21.11
+ Ours	1:3	39.0	64.2	40.0	56.07	24.36
CC-Diff [ 35 ]	1:2	38.8	65.4	39.7	54.75	21.44
+ Ours	1:2	39.3	64.4	41.2	55.98	22.34
CC-Diff [ 35 ]	1:1	37.4	63.2	38.6	54.80	20.11

Table 3: Comparison of downstream object detection performance under different real-to-synthetic data ratios. All experiments are conducted on the DOTA dataset utilizing the CC-Diff [ 35 ] baseline.

Figure 5: Qualitative comparison of generated instances with and without the Edge2Edge module. Incorporating the Edge2Edge module introduces rich structural variations, significantly enhancing both the structural and overall diversity of the generated instances within the specified bounding boxes.

Method	FID $\downarrow$	YOLOScore $\uparrow$
Method	FID $\downarrow$	$\text{mAP}_{50}$	$\text{mAP}_{50-95}$
w/o Edge2Edge	68.40	69.5	46.9
w/ Edge2Edge	68.12	70.1	47.7

Table 4: Ablation of the Edge2Edge module on DIOR-RSVG. While maintaining nearly identical generation fidelity (FID), the module significantly boosts layout consistency (YOLOScore), particularly in the stricter IoU threshold.

Method	vehicle	ship	basketballcourt	groundtrackfield	harbor	tenniscourt	airplane
w/o Edge2Edge	6.8	8.3	14.9	13.6	9.6	42.6	8.2
w/ Edge2Edge	7.1	8.3	15.5	13.5	10.2	44.6	8.4

Table 5: Cross-dataset validation. Models are trained on DIOR-RSVG and evaluated on DOTA. The Edge2Edge module improves AP across most categories, demonstrating its strong generalization capability, which is crucial for practical applications.

Method	FID $\downarrow$	YOLOScore $\uparrow$
Method	FID $\downarrow$	$\text{mAP}_{50}$	$\text{mAP}_{50-95}$
Baseline (Global Control)	112.43	62.1	45.5
+ Spatially Gated	64.38	68.7	45.7
+ High-pass Filter (Full)	68.40	69.5	46.9

Table 6: Ablation of the FGControl module on the DIOR-RSVG dataset. The spatially gated mechanism resolves background confusion, while the high-pass filter further decouples features to achieve the highest layout consistency.

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RSEdit: Text-Guided Image Editing for Remote Sensing

Chen Zhenyuan, Zhang Zechuan, Zhang Feng

School of Earth Sciences, Zhejiang University, Hangzhou, 310058 China, and Zhejiang Provincial Key Laboratory of Geographic Information Science. · ReLER, CCAI, Zhejiang University, Hangzhou, China. · Key Laboratory of Spatio-temporal Information and Intelligent Services (LSIIS), Ministry of Natural Resources of the People’s Republic of China.

Text-Guided Image Editing Diffusion-Based Image Editing T2I Diffusion Models

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In this paper, we explore text-guided image editing in the remote sensing domain using generative modeling. We propose \rsedit, a collection of models from U-Net to DiT with various configurations. Specifically, we present the first comprehensive study of conditioning strategies for building image editing models from off-the-shelf text-to-image ones. Our experiments show that \rsedit achieves the best instruction-faithful edits while preserving geospatial structure. We release the code at https://github.com/Bili-Sakura/RSEdit-Preview and checkpoints at https://huggingface.co/collections/BiliSakura/rsedit.

Abstract

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