GeoDec-Net: A Geometry-Decomposed Network for Oriented Object Detection in Remote Sensing Images

This repository provides the official PyTorch implementation of the paper "GeoDec-Net: A Geometry Decomposed Oriented Bounding Boxes Object Detection Network for Remote Sensing Images". GeoDec-Net is a lightweight single-stage oriented object detector designed for remote sensing imagery, incorporating three core modules: the Geometry-Decomposed Block (GDBlock) for multi-dimensional geometric feature extraction, Geometry-Decomposed Pooling (GDPool) for anisotropic long-range context modeling of slender objects, and Geometry-Decomposed Fusion (GDFusion) for high-resolution directional feature refinement and background noise suppression. This archive contains the complete network architecture code, model configuration files, and scripts for training, validation, and inference latency benchmarking on the DOTA-v1.0 and HRSC2016 datasets.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-26
DOI
https://doi.org/10.5281/zenodo.23673416
Primary Topic
Advanced Neural Network Applications
Type
article
Field-Weighted Citation Impact
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article

GeoDec-Net: A Geometry-Decomposed Network for Oriented Object Detection in Remote Sensing Images

Zitong Gao
Zenodo (CERN European Organization for Nuclear Research)
Advanced Neural Network Applications
article

GeoDec-Net: A Geometry-Decomposed Network for Oriented Object Detection in Remote Sensing Images

Zitong Gao
article en

Abstract

This repository provides the official PyTorch implementation of the paper "GeoDec-Net: A Geometry Decomposed Oriented Bounding Boxes Object Detection Network for Remote Sensing Images". GeoDec-Net is a lightweight single-stage oriented object detector designed for remote sensing imagery, incorporating three core modules: the Geometry-Decomposed Block (GDBlock) for multi-dimensional geometric feature extraction, Geometry-Decomposed Pooling (GDPool) for anisotropic long-range context modeling of slender objects, and Geometry-Decomposed Fusion (GDFusion) for high-resolution directional feature refinement and background noise suppression. This archive contains the complete network architecture code, model configuration files, and scripts for training, validation, and inference latency benchmarking on the DOTA-v1.0 and HRSC2016 datasets.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 17%
Advanced Neural Network Applications
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