# Active learning for object detection in high-resolution satellite images

arXiv: https://arxiv.org/abs/2101.02480 · HTML: https://mlsanity.com/paper/2101.02480/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [Adversarial Active Learning for Deep Networks: a Margin Based Approach](https://mlsanity.com/paper/1802.09841/) (2018)
- [A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1910.02923/) (2019)
- [Discriminative Active Learning](https://mlsanity.com/paper/1907.06347/) (2019)
- [Reinforced active learning for image segmentation](https://mlsanity.com/paper/2002.06583/) (2020)

## Built on by (most cited first, 5 of 5)

- [Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing Images](https://mlsanity.com/paper/2310.05498/) (2023)
- [SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios](https://mlsanity.com/paper/2408.04482/) (2024)
- [Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning](https://mlsanity.com/paper/2202.04890/) (2022)
- [Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery](https://mlsanity.com/paper/2403.02736/) (2024)
- [Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement](https://mlsanity.com/paper/2504.16136/) (2025)

## Abstract (arXiv)

In machine learning, the term active learning regroups techniques that aim at selecting the most useful data to label from a large pool of unlabelled examples. While supervised deep learning techniques have shown to be increasingly efficient on many applications, they require a huge number of labelled examples to reach operational performances. Therefore, the labelling effort linked to the creation of the datasets required is also increasing. When working on defense-related remote sensing applications, labelling can be challenging due to the large areas covered and often requires military experts who are rare and whose time is primarily dedicated to operational needs. Limiting the labelling effort is thus of utmost importance. This study aims at reviewing the most relevant active learning techniques to be used for object detection on very high resolution imagery and shows an example of the value of such techniques on a relevant operational use case: aircraft detection.