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Updated: Jan 7, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI

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Published on: February 3, 2015

TopoRF-Net: Topology-Aware Road Segmentation in Multi-Resolution Remote Sensing via Multi-Receptive Field Adaptation.

Junjie Fu¹, Chenliang Wang², Hongchen Lv¹

¹SuperMap Software Co., Ltd., Beijing 100015, China.

Sensors (Basel, Switzerland)

|December 31, 2025

Summary

This summary is machine-generated.

This study introduces TopoRF-Net, a novel framework for road extraction from remote sensing images. It enhances road network connectivity and topological integrity, outperforming existing methods in precision and continuity.

Keywords:

multi-receptive field remote sensing imagery road extraction semantic segmentation structural connectivity topology-aware loss

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Area of Science:

Remote Sensing
Computer Vision
Geographic Information Systems

Background:

Road extraction from multi-resolution remote sensing imagery presents challenges due to sparse, elongated, and complex road structures.
Existing semantic segmentation models often prioritize pixel-level accuracy over topological integrity, leading to discontinuous road predictions.

Purpose of the Study:

To propose an end-to-end road extraction framework that addresses the limitations of current methods by focusing on structural connectivity and topological integrity.
To improve the continuity and accuracy of road network extraction in remote sensing data.

Main Methods:

Developed a framework incorporating a multi-receptive-field module to capture road patterns at various scales.
Implemented a connectivity-aware decoding mechanism to enhance structural coherence.
Utilized a topology-aware loss function to guide the restoration of continuous road networks during training.

Main Results:

TopoRF-Net achieved high performance on the DeepGlobe-Road dataset (OA 98.57%, IoU 69.76%, F1 82.18%) and the Massachusetts dataset (OA 96.65%, IoU 59.68%, F1 74.75%).
The proposed method significantly outperformed existing approaches in both precision and connectivity metrics.
Demonstrated favorable parameter efficiency and inference performance.

Conclusions:

The proposed TopoRF-Net effectively extracts road networks with improved topological integrity and continuity.
The framework offers a significant advancement in road extraction from remote sensing imagery, addressing key limitations of previous methods.
The results highlight the importance of connectivity-aware modeling and topology-aware losses for accurate road network segmentation.