# Ensemble Learning techniques for object detection in high-resolution satellite images

Source: [Ensemble Learning techniques for object detection in high-resolution satellite images](<https://www.sota2.com/research/paper/68fa6bd71e209131cea307d1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:13:32.751Z

Authors: Arthur Vilhelm, Matthieu Limbert, Cl\\'ement Audebert, Tugdual Ceillier

Published: 2022-02-16T10:19:21.000Z

## Abstract

Ensembling is a method that aims to maximize the detection performance by fusing individual detectors. While rarely mentioned in deep-learning articles applied to remote sensing, ensembling methods have been widely used to achieve high scores in recent data science com-petitions,  such  as  Kaggle.  The  few  remote  sensing  articles  mentioning ensembling mainly focus on mid resolution images and earth observation applications such as land use classification, but never on Very High Resolution (VHR) images for defense-related applications or object detection.This study aims at reviewing the most relevant ensembling 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 (vehicle detection in desert areas).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2202.10554>)

[PDF](<https://arxiv.org/pdf/2202.10554.pdf>)