# Combining Transformers and CNNs for Efficient Object Detection in High-Resolution Satellite Imagery

Source: [Combining Transformers and CNNs for Efficient Object Detection in High-Resolution Satellite Imagery](<https://www.sota2.com/research/paper/68fa7a101e209131cea80e62>)

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

Data updated: 2025-10-28T13:57:19.237Z

Authors: Nicolas Drapier, Aladine Chetouani, Aur\\'elien Chateigner

Published: 2025-07-15T07:10:34.000Z

## Abstract

We present GLOD, a transformer-first architecture for object detection in high-resolution satellite imagery. GLOD replaces CNN backbones with a Swin Transformer for end-to-end feature extraction, combined with novel UpConvMixer blocks for robust upsampling and Fusion Blocks for multi-scale feature integration. Our approach achieves 32.95\\% on xView, outperforming SOTA methods by 11.46\\%. Key innovations include asymmetric fusion with CBAM attention and a multi-path head design capturing objects across scales. The architecture is optimized for satellite imagery challenges, leveraging spatial priors while maintaining computational efficiency.

## 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/2507.11040>)

[PDF](<https://arxiv.org/pdf/2507.11040.pdf>)