# RarePlanes: Synthetic Data Takes Flight

arXiv: https://arxiv.org/abs/2006.02963 · HTML: https://mlsanity.com/paper/2006.02963/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 30 of 48)

- [Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges](https://mlsanity.com/paper/2102.12219/) (2021)
- [Dense Nested Attention Network for Infrared Small Target Detection](https://mlsanity.com/paper/2106.00487/) (2021)
- [MTU-Net: Multi-level TransUNet for Space-based Infrared Tiny Ship Detection](https://mlsanity.com/paper/2209.13756/) (2022)
- [SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection](https://mlsanity.com/paper/2309.01907/) (2023)
- [Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives](https://mlsanity.com/paper/2505.14361/) (2025)
- [SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models](https://mlsanity.com/paper/2309.16812/) (2023)
- [SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery](https://mlsanity.com/paper/2406.18151/) (2024)
- [Infrared Small Target Detection in Satellite Videos: A New Dataset and A Novel Recurrent Feature Refinement Framework](https://mlsanity.com/paper/2409.12448/) (2024)
- [Applications of Knowledge Distillation in Remote Sensing: A Survey](https://mlsanity.com/paper/2409.12111/) (2024)
- [SyntEO: Synthetic Data Set Generation for Earth Observation and Deep Learning -- Demonstrated for Offshore Wind Farm Detection](https://mlsanity.com/paper/2112.02829/) (2021)
- [FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression](https://mlsanity.com/paper/2403.16677/) (2024)
- [Improving Object Detector Training on Synthetic Data by Starting With a Strong Baseline Methodology](https://mlsanity.com/paper/2405.19822/) (2024)
- [SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems](https://mlsanity.com/paper/2106.15681/) (2021)
- [Whale Detection Enhancement through Synthetic Satellite Images](https://mlsanity.com/paper/2308.07766/) (2023)
- [Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction](https://mlsanity.com/paper/2505.06905/) (2025)
- [Artificial and beneficial -- Exploiting artificial images for aerial vehicle detection](https://mlsanity.com/paper/2104.03054/) (2021)
- [Aerial Image Object Detection With Vision Transformer Detector (ViTDet)](https://mlsanity.com/paper/2301.12058/) (2023)
- [Foundation Models for Remote Sensing: An Analysis of MLLMs for Object Localization](https://mlsanity.com/paper/2504.10727/) (2025)
- [The MIS Check-Dam Dataset for Object Detection and Instance Segmentation Tasks](https://mlsanity.com/paper/2111.15613/) (2021)
- [The Weaknesses of Adversarial Camouflage in Overhead Imagery](https://mlsanity.com/paper/2207.02963/) (2022)
- [Centerpoints Are All You Need in Overhead Imagery](https://mlsanity.com/paper/2210.01857/) (2022)
- [Towards a Taxonomy for the Use of Synthetic Data in Advanced Analytics](https://mlsanity.com/paper/2212.02622/) (2022)
- [SSTD: Stripe-Like Space Target Detection Using Single-Point Weak Supervision](https://mlsanity.com/paper/2407.18097/) (2024)
- [Generating Synthetic Satellite Imagery for Rare Objects: An Empirical Comparison of Models and Metrics](https://mlsanity.com/paper/2409.01138/) (2024)
- [Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches](https://mlsanity.com/paper/2507.14633/) (2025)
- [SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation](https://mlsanity.com/paper/2510.06596/) (2025)
- [A benchmark dataset for deep learning-based airplane detection: HRPlanes](https://mlsanity.com/paper/2204.10959/) (2022)
- [Meta-simulation for the Automated Design of Synthetic Overhead Imagery](https://mlsanity.com/paper/2209.08685/) (2022)
- [Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models](https://mlsanity.com/paper/2211.11797/) (2022)
- [FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery](https://mlsanity.com/paper/2404.02877/) (2024)

## Abstract (arXiv)

RarePlanes is a unique open-source machine learning dataset that incorporates both real and synthetically generated satellite imagery. The RarePlanes dataset specifically focuses on the value of synthetic data to aid computer vision algorithms in their ability to automatically detect aircraft and their attributes in satellite imagery. Although other synthetic/real combination datasets exist, RarePlanes is the largest openly-available very-high resolution dataset built to test the value of synthetic data from an overhead perspective. Previous research has shown that synthetic data can reduce the amount of real training data needed and potentially improve performance for many tasks in the computer vision domain. The real portion of the dataset consists of 253 Maxar WorldView-3 satellite scenes spanning 112 locations and 2,142 km^2 with 14,700 hand-annotated aircraft. The accompanying synthetic dataset is generated via AI.Reverie's simulation platform and features 50,000 synthetic satellite images simulating a total area of 9331.2 km^2 with ~630,000 aircraft annotations. Both the real and synthetically generated aircraft feature 10 fine grain attributes including: aircraft length, wingspan, wing-shape, wing-position, wingspan class, propulsion, number of engines, number of vertical-stabilizers, presence of canards, and aircraft role. Finally, we conduct extensive experiments to evaluate the real and synthetic datasets and compare performances. By doing so, we show the value of synthetic data for the task of detecting and classifying aircraft from an overhead perspective.