# Multimodal Object Detection in Remote Sensing

Source: [Multimodal Object Detection in Remote Sensing](<https://www.sota2.com/research/paper/68fa700a1e209131cea48cd2>)

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

Data updated: 2025-10-26T01:32:16.690Z

Authors: Abdelbadie Belmouhcine, Jean-Christophe Burnel, Luc Courtrai, Minh-Tan Pham, S\\'ebastien Lef\\\`evre

Published: 2023-07-13T12:37:14.000Z

## Abstract

Object detection in remote sensing is a crucial computer vision task that has seen significant advancements with deep learning techniques. However, most existing works in this area focus on the use of generic object detection and do not leverage the potential of multimodal data fusion. In this paper, we present a comparison of methods for multimodal object detection in remote sensing, survey available multimodal datasets suitable for evaluation, and discuss future directions.

## 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/2307.06724>)

[PDF](<https://arxiv.org/pdf/2307.06724.pdf>)