# You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

arXiv: https://arxiv.org/abs/1805.09512 · HTML: https://mlsanity.com/paper/1805.09512/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2018.

## Built on by (most cited first, 27 of 27)

- [Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning](https://mlsanity.com/paper/2402.01444/) (2024)
- [Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagery](https://mlsanity.com/paper/2112.02991/) (2021)
- [Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances](https://mlsanity.com/paper/2309.06751/) (2023)
- [Cascaded Zoom-in Detector for High Resolution Aerial Images](https://mlsanity.com/paper/2303.08747/) (2023)
- [The SpaceNet Multi-Temporal Urban Development Challenge](https://mlsanity.com/paper/2102.11958/) (2021)
- [A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations](https://mlsanity.com/paper/2108.08882/) (2021)
- [Semi-supervised Open-World Object Detection](https://mlsanity.com/paper/2402.16013/) (2024)
- [Efficient High-Resolution Deep Learning: A Survey](https://mlsanity.com/paper/2207.13050/) (2022)
- [Oriented Bounding Boxes for Small and Freely Rotated Objects](https://mlsanity.com/paper/2104.11854/) (2021)
- [C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation](https://mlsanity.com/paper/2411.10788/) (2024)
- [URCDM: Ultra-Resolution Image Synthesis in Histopathology](https://mlsanity.com/paper/2407.13277/) (2024)
- [ADG-Pose: Automated Dataset Generation for Real-World Human Pose Estimation](https://mlsanity.com/paper/2202.00753/) (2022)
- [Interactive Image-Based Aphid Counting in Yellow Water Traps under Stirring Actions](https://mlsanity.com/paper/2411.10357/) (2024)
- [RS-YOLOX: A High Precision Detector for Object Detection in Satellite Remote Sensing Images](https://mlsanity.com/paper/2502.02850/) (2025)
- [Bounding Box-Free Instance Segmentation Using Semi-Supervised Learning for Generating a City-Scale Vehicle Dataset](https://mlsanity.com/paper/2111.12122/) (2021)
- [Learning High-quality Proposals for Acne Detection](https://mlsanity.com/paper/2207.03674/) (2022)
- [Vehicle Vectors and Traffic Patterns from Planet Imagery](https://mlsanity.com/paper/2406.06320/) (2024)
- [On the Feasibility and Opportunity of Autoregressive 3D Object Detection](https://mlsanity.com/paper/2603.07985/) (2026)
- [SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery](https://mlsanity.com/paper/2108.12534/) (2021)
- [Airplane Type Identification Based on Mask RCNN and Drone Images](https://mlsanity.com/paper/2108.12811/) (2021)
- [RelationRS: Relationship Representation Network for Object Detection in Aerial Images](https://mlsanity.com/paper/2110.06730/) (2021)
- [Fast Fourier Convolution Based Remote Sensor Image Object Detection for Earth Observation](https://mlsanity.com/paper/2209.00551/) (2022)
- [Automatic detection of aerial survey ground control points based on Yolov5-OBB](https://mlsanity.com/paper/2303.03041/) (2023)
- [Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis in Histopathology](https://mlsanity.com/paper/2312.01152/) (2023)
- [A GAN-Enhanced Deep Learning Framework for Rooftop Detection from Historical Aerial Imagery](https://mlsanity.com/paper/2503.23200/) (2025)
- [Scalable Dynamic Origin-Destination Demand Estimation Enhanced by High-Resolution Satellite Imagery Data](https://mlsanity.com/paper/2506.22499/) (2025)
- [SeFaR: Semantic Feature-aware Robustness Testing of Deep Neural Networks](https://mlsanity.com/paper/2608.10289/) (2026)

## Abstract (arXiv)

Detection of small objects in large swaths of imagery is one of the primary problems in satellite imagery analytics. While object detection in ground-based imagery has benefited from research into new deep learning approaches, transitioning such technology to overhead imagery is nontrivial. Among the challenges is the sheer number of pixels and geographic extent per image: a single DigitalGlobe satellite image encompasses >64 km2 and over 250 million pixels. Another challenge is that objects of interest are minuscule (often only ~10 pixels in extent), which complicates traditional computer vision techniques. To address these issues, we propose a pipeline (You Only Look Twice, or YOLT) that evaluates satellite images of arbitrary size at a rate of >0.5 km2/s. The proposed approach can rapidly detect objects of vastly different scales with relatively little training data over multiple sensors. We evaluate large test images at native resolution, and yield scores of F1 > 0.8 for vehicle localization. We further explore resolution and object size requirements by systematically testing the pipeline at decreasing resolution, and conclude that objects only ~5 pixels in size can still be localized with high confidence. Code is available at https://github.com/CosmiQ/yolt.