# Ship classification from overhead imagery using synthetic data and domain adaptation

arXiv: https://arxiv.org/abs/1905.03894 · HTML: https://mlsanity.com/paper/1905.03894/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2019.

## Built on by (most cited first, 6 of 6)

- [Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environment](https://mlsanity.com/paper/2210.08487/) (2022)
- [Image and AIS Data Fusion Technique for Maritime Computer Vision Applications](https://mlsanity.com/paper/2312.05270/) (2023)
- [Synthetic Data for Feature Selection](https://mlsanity.com/paper/2211.03035/) (2022)
- [Synthetic Data for Object Classification in Industrial Applications](https://mlsanity.com/paper/2212.04790/) (2022)
- [Meta-simulation for the Automated Design of Synthetic Overhead Imagery](https://mlsanity.com/paper/2209.08685/) (2022)
- [Towards Training Music Taggers on Synthetic Data](https://mlsanity.com/paper/2407.02156/) (2024)

## Abstract (arXiv)

In this paper, we revisit the problem of classifying ships (maritime vessels) detected from overhead imagery. Despite the last decade of research on this very important and pertinent problem, it remains largely unsolved. One of the major issues with the detection and classification of ships and other objects in the maritime domain is the lack of substantial ground truth data needed to train state-of-the-art machine learning algorithms. We address this issue by building a large (200k) synthetic image dataset using the Unity gaming engine and 3D ship models. We demonstrate that with the use of synthetic data, classification performance increases dramatically, particularly when there are very few annotated images used in training.