Laplacian Frequency Interaction Network for Rural Thematic Road Extraction
2026-05-04 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors present LFINet, a method to map rural roads from noisy and sparse images created by farm machinery movements. Their approach splits images into parts showing overall shapes and fine details, then processes these parts separately to keep road features clear. They combine these parts carefully to build accurate road maps that are consistent and less broken. Tested on real agricultural data from China, their method performed slightly better than previous ones in identifying road networks.
topological road networksmovement trajectory imagesLaplacian Multi-scale SeparatorCross-Frequency InteractionHigh-Frequency BlockSpatial TransformerFrequency Gated ModulationProgressive Reconstruction DecoderF1-scoreIntersection over Union
Authors
Baiyan Chen, Weixin Zhai
Abstract
Rural thematic road network construction aims to extract topological road structures from movement trajectory images of agricultural machinery. However, this task faces challenges where downsampling methods commonly used in existing studies tend to blur the sparse high-frequency road structures, and the heavy noise from dense field operations often leads to fragmented or redundant topologies in the extracted networks. To address these challenges, we propose LFINet, a Laplacian Frequency Interaction Network. The network begins with a Laplacian Multi-scale Separator (LMS) to decouple the image into low-frequency semantic contexts and high-frequency structural details. These components are then processed by the Cross-Frequency Interaction Block (CFIB) through a dual-pathway architecture in which a High-Frequency Block (HFB) refines local structures while a Spatial Transformer (ST) captures global semantics. Subsequently, a Frequency Gated Modulation (FGM) mechanism integrates the features from pathways by leveraging semantic contexts to calibrate the structural details. Finally, a Progressive Reconstruction Decoder iteratively fuses multi-scale features to ensure topological consistency. Experiments conducted on a real-world agricultural trajectories dataset from Henan Province, China, show that LFINet establishes a new state-of-the-art. Specifically, it achieves an F1-score of 92.54% and an IoU of 86.12%, surpassing the second-ranked method by 0.64% and 1.1%, respectively. This confirms its capability to effectively construct topological road networks from noisy and sparse field data.