Reverse Refinement Network for Narrow Rural Road Detection in High-Resolution Satellite Imagery

TL;DR

This paper introduces R2-Net, a novel neural network designed to accurately detect narrow rural roads in high-resolution satellite images, addressing the unique challenges posed by their irregular and fine features.

Contribution

The paper presents a reverse refinement network with specialized modules for enhanced rural road detection, improving connectivity and detail preservation over existing methods.

Findings

R2-Net outperforms state-of-the-art methods on multiple datasets.

It achieves higher accuracy in detecting narrow rural roads.

The model is effective for large-scale rural road mapping.

Abstract

The automated extraction of rural roads is pivotal for rural development and transportation planning, serving as a cornerstone for socio-economic progress. Current research primarily focuses on road extraction in urban areas. However, rural roads present unique challenges due to their narrow and irregular nature, posing significant difficulties for road extraction. In this article, a reverse refinement network (R2-Net) is proposed to extract narrow rural roads, enhancing their connectivity and distinctiveness from the background. Specifically, to preserve the fine details of roads within high-resolution feature maps, R2-Net utilizes an axis context aware module (ACAM) to capture the long-distance spatial context information in various layers. Subsequently, the multi-level features are aggregated through a global aggregation module (GAM). Moreover, in the decoder stage, R2-Net employs a reverse-aware module (RAM) to direct the attention of the network to the complex background, thus amplifying its separability. In experiments, we compare R2-Net with several state-of-the-art methods using the DeepGlobe road extraction dataset and the WHU-RuR+ global large-scale rural road dataset. R2-Net achieved superior performance and especially excelled in accurately detecting narrow roads. Furthermore, we explored the applicability of R2-Net for large-scale rural road mapping. The results show that the proposed R2-Net has significant performance advantages for large-scale rural road mapping applications.