# Concurrent Segmentation and Object Detection CNNs for Aircraft Detection and Identification in Satellite Images

arXiv: https://arxiv.org/abs/2005.13215 · HTML: https://mlsanity.com/paper/2005.13215/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 3 of 3)

- [Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning](https://mlsanity.com/paper/2202.04890/) (2022)
- [Measuring economic activity from space: a case study using flying airplanes and COVID-19](https://mlsanity.com/paper/2104.10345/) (2021)
- [Convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture](https://mlsanity.com/paper/2502.05476/) (2025)

## Abstract (arXiv)

Detecting and identifying objects in satellite images is a very challenging task: objects of interest are often very small and features can be difficult to recognize even using very high resolution imagery. For most applications, this translates into a trade-off between recall and precision. We present here a dedicated method to detect and identify aircraft, combining two very different convolutional neural networks (CNNs): a segmentation model, based on a modified U-net architecture, and a detection model, based on the RetinaNet architecture. The results we present show that this combination outperforms significantly each unitary model, reducing drastically the false negative rate.