# On Learning Vehicle Detection in Satellite Video

arXiv: https://arxiv.org/abs/2001.10900 · HTML: https://mlsanity.com/paper/2001.10900/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark](https://mlsanity.com/paper/2111.12960/) (2021)
- [Deep Vehicle Detection in Satellite Video](https://mlsanity.com/paper/2204.06828/) (2022)
- [Deep Learning Enhanced Road Traffic Analysis: Scalable Vehicle Detection and Velocity Estimation Using PlanetScope Imagery](https://mlsanity.com/paper/2410.14698/) (2024)
- [HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery](https://mlsanity.com/paper/2110.09881/) (2021)

## Abstract (arXiv)

Vehicle detection in aerial and satellite images is still challenging due to their tiny appearance in pixels compared to the overall size of remote sensing imagery. Classical methods of object detection very often fail in this scenario due to violation of implicit assumptions made such as rich texture, small to moderate ratios between image size and object size. Satellite video is a very new modality which introduces temporal consistency as inductive bias. Approaches for vehicle detection in satellite video use either background subtraction, frame differencing or subspace methods showing moderate performance (0.26 - 0.82 F1 score). This work proposes to apply recent work on deep learning for wide-area motion imagery (WAMI) on satellite video. We show in a first approach comparable results (0.84 F1) on Planet's SkySat-1 LasVegas video with room for further improvement.