# Stable Diffusion For Aerial Object Detection

arXiv: https://arxiv.org/abs/2311.12345 · HTML: https://mlsanity.com/paper/2311.12345/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 6 of its 7 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 6 of 6)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges](https://mlsanity.com/paper/2102.12219/) (2021)
- [Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection](https://mlsanity.com/paper/2202.06934/) (2022)

## Built on by (most cited first, 10 of 10)

- [GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots](https://mlsanity.com/paper/2404.15500/) (2024)
- [Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives](https://mlsanity.com/paper/2404.08926/) (2024)
- [Multi-Agent Geospatial Copilots for Remote Sensing Workflows](https://mlsanity.com/paper/2501.16254/) (2025)
- [Evaluating Tool-Augmented Agents in Remote Sensing Platforms](https://mlsanity.com/paper/2405.00709/) (2024)
- [GeckOpt: LLM System Efficiency via Intent-Based Tool Selection](https://mlsanity.com/paper/2404.15804/) (2024)
- [Geo-OLM: Enabling Sustainable Earth Observation Studies with Cost-Efficient Open Language Models & State-Driven Workflows](https://mlsanity.com/paper/2504.04319/) (2025)
- [RISE: Enhancing VLM Image Annotation with Self-Supervised Reasoning](https://mlsanity.com/paper/2508.13229/) (2025)
- [Structural MRI Synthesis for Alzheimer's Disease via Conditional Diffusion on Anatomical Masks](https://mlsanity.com/paper/2606.18354/) (2026)
- [UniScale: Arbitrary-Scale Industrial Anomaly Generation](https://mlsanity.com/paper/2608.07864/) (2026)
- [Improving Semantic Coverage in AI Test & Evaluation through Generative Augmentation](https://mlsanity.com/paper/2610.13697/) (2026)

## Abstract (arXiv)

Aerial object detection is a challenging task, in which one major obstacle lies in the limitations of large-scale data collection and the long-tail distribution of certain classes. Synthetic data offers a promising solution, especially with recent advances in diffusion-based methods like stable diffusion (SD). However, the direct application of diffusion methods to aerial domains poses unique challenges: stable diffusion's optimization for rich ground-level semantics doesn't align with the sparse nature of aerial objects, and the extraction of post-synthesis object coordinates remains problematic. To address these challenges, we introduce a synthetic data augmentation framework tailored for aerial images. It encompasses sparse-to-dense region of interest (ROI) extraction to bridge the semantic gap, fine-tuning the diffusion model with low-rank adaptation (LORA) to circumvent exhaustive retraining, and finally, a Copy-Paste method to compose synthesized objects with backgrounds, providing a nuanced approach to aerial object detection through synthetic data.