# AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data Generation

arXiv: https://arxiv.org/abs/2411.15497 · HTML: https://mlsanity.com/paper/2411.15497/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 51 references that are in the map.
- First posted on arXiv 23 Nov 2024.

## Builds on (most cited first, 30 of 40)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://mlsanity.com/paper/2301.07093/) (2023)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [RemoteCLIP: A Vision Language Foundation Model for Remote Sensing](https://mlsanity.com/paper/2306.11029/) (2023)
- [Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark](https://mlsanity.com/paper/1909.00133/) (2019)
- [LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation](https://mlsanity.com/paper/2303.17189/) (2023)
- [Oriented R-CNN for Object Detection](https://mlsanity.com/paper/2108.05699/) (2021)
- [Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection](https://mlsanity.com/paper/1708.01642/) (2017)
- [Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer](https://mlsanity.com/paper/2312.09008/) (2023)
- [ReCo: Region-Controlled Text-to-Image Generation](https://mlsanity.com/paper/2211.15518/) (2022)
- [InstanceDiffusion: Instance-level Control for Image Generation](https://mlsanity.com/paper/2402.03290/) (2024)
- [StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models](https://mlsanity.com/paper/2308.07863/) (2023)
- [Classification Accuracy Score for Conditional Generative Models](https://mlsanity.com/paper/1905.10887/) (2019)
- [DiffusionSat: A Generative Foundation Model for Satellite Imagery](https://mlsanity.com/paper/2312.03606/) (2023)
- [MIGC: Multi-Instance Generation Controller for Text-to-Image Synthesis](https://mlsanity.com/paper/2402.05408/) (2024)
- [Anchor-free Oriented Proposal Generator for Object Detection](https://mlsanity.com/paper/2110.01931/) (2021)
- [Image Synthesis From Reconfigurable Layout and Style](https://mlsanity.com/paper/1908.07500/) (2019)
- [Augmentation for small object detection](https://mlsanity.com/paper/1902.07296/) (2019)
- [MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion](https://mlsanity.com/paper/2203.12621/) (2022)
- [GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation](https://mlsanity.com/paper/2306.04607/) (2023)
- [CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Model](https://mlsanity.com/paper/2403.11614/) (2024)

## Built on by (most cited first, 30 of 35)

- [DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo](https://mlsanity.com/paper/2412.11578/) (2024)
- [EarthSynth: Generating Informative Earth Observation with Diffusion Models](https://mlsanity.com/paper/2505.12108/) (2025)
- [CC-Diff: Enhancing Contextual Coherence in Remote Sensing Image Synthesis](https://mlsanity.com/paper/2412.08464/) (2024)
- [What if Agents Could Imagine? Reinforcing Open-Vocabulary HOI Comprehension through Generation](https://mlsanity.com/paper/2602.11499/) (2026)
- [From Scale to Speed: Adaptive Test-Time Scaling for Image Editing](https://mlsanity.com/paper/2603.00141/) (2026)
- [Neptune-X: Active X-to-Maritime Generation for Universal Maritime Object Detection](https://mlsanity.com/paper/2509.20745/) (2025)
- [Object Fidelity Diffusion for Remote Sensing Image Generation](https://mlsanity.com/paper/2508.10801/) (2025)
- [TerraGen: A Unified Multi-Task Layout Generation Framework for Remote Sensing Data Augmentation](https://mlsanity.com/paper/2510.21391/) (2025)
- [FICGen: Frequency-Inspired Contextual Disentanglement for Layout-driven Degraded Image Generation](https://mlsanity.com/paper/2509.01107/) (2025)
- [CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentation](https://mlsanity.com/paper/2501.15389/) (2025)
- [Edge-case Synthesis for Fisheye Object Detection: A Data-centric Perspective](https://mlsanity.com/paper/2507.16254/) (2025)
- [Class-specific diffusion models improve military object detection in a low-data domain](https://mlsanity.com/paper/2604.18076/) (2026)
- [ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Sensing](https://mlsanity.com/paper/2507.04678/) (2025)
- [Task-Oriented Data Synthesis and Control-Rectify Sampling for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2512.16740/) (2025)
- [IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools](https://mlsanity.com/paper/2605.20682/) (2026)
- [Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation](https://mlsanity.com/paper/2605.31266/) (2026)
- [TerraDiT-Omega: Unified Spatial Control for Satellite Image Synthesis with Any Geospatial Primitive](https://mlsanity.com/paper/2606.31029/) (2026)
- [Visual Prototype Conditioned Focal Region Generation for UAV-Based Object Detection](https://mlsanity.com/paper/2604.02966/) (2026)
- [AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors](https://mlsanity.com/paper/2605.14341/) (2026)
- [CEIDM: A Controlled Entity and Interaction Diffusion Model for Enhanced Text-to-Image Generation](https://mlsanity.com/paper/2508.17760/) (2025)
- [RSGen: Enhancing Layout-Driven Remote Sensing Image Generation with Diverse Edge Guidance](https://mlsanity.com/paper/2603.15484/) (2026)
- [Generating Satellite Imagery Data for Wildfire Detection through Mask-Conditioned Generative AI](https://mlsanity.com/paper/2604.02479/) (2026)
- [HarmoniDiff-RS: Training-Free Diffusion Harmonization for Satellite Image Composition](https://mlsanity.com/paper/2604.19392/) (2026)
- [HIR-ALIGN: Enhancing Hyperspectral Image Restoration via Diffusion-Based Data Generation](https://mlsanity.com/paper/2605.13581/) (2026)
- [D2-CDIG: Controlled Diffusion Remote Sensing Image Generation with Dual Priors of DEM and Cloud-Fog](https://mlsanity.com/paper/2605.14326/) (2026)
- [Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Network](https://mlsanity.com/paper/2605.16764/) (2026)
- [What Makes Synthetic Data Effective in Image Segmentation](https://mlsanity.com/paper/2605.19289/) (2026)
- [Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting](https://mlsanity.com/paper/2607.02637/) (2026)
- [FlowForm: Synergizing Fluid Physics with Topological Consistency for Satellite Flood Synthesis](https://mlsanity.com/paper/2608.03822/) (2026)
- [Synthetic Data Augmentation for Satellite-Based Analysis of Battle-Damaged Agricultural Fields in Ukraine](https://mlsanity.com/paper/2608.16380/) (2026)

## Abstract (arXiv)

Remote sensing image object detection (RSIOD) aims to identify and locate specific objects within satellite or aerial imagery. However, there is a scarcity of labeled data in current RSIOD datasets, which significantly limits the performance of current detection algorithms. Although existing techniques, e.g., data augmentation and semi-supervised learning, can mitigate this scarcity issue to some extent, they are heavily dependent on high-quality labeled data and perform worse in rare object classes. To address this issue, this paper proposes a layout-controllable diffusion generative model (i.e. AeroGen) tailored for RSIOD. To our knowledge, AeroGen is the first model to simultaneously support horizontal and rotated bounding box condition generation, thus enabling the generation of high-quality synthetic images that meet specific layout and object category requirements. Additionally, we propose an end-to-end data augmentation framework that integrates a diversity-conditioned generator and a filtering mechanism to enhance both the diversity and quality of generated data. Experimental results demonstrate that the synthetic data produced by our method are of high quality and diversity. Furthermore, the synthetic RSIOD data can significantly improve the detection performance of existing RSIOD models, i.e., the mAP metrics on DIOR, DIOR-R, and HRSC datasets are improved by 3.7%, 4.3%, and 2.43%, respectively. The code is available at https://github.com/Sonettoo/AeroGen.