Automatic Extraction of Roads From Multisource Geospatial Data Using Fusion Attention Network and Regularization Algorithm

Automatic extraction of roads from remote sensing imagery has numerous applications, such as urban planning and navigation. However, the quality of images is limited, and most existing road extraction methods suffer from discontinuity. Additionally, there remains a gap between pixel-based road segme...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 17
Main Authors: Wang, Zejiao, Xiang, Longgang, Liu, Zhongyu, Wang, Zhengxiang
Format: Journal Article
Language:English
Published: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Data mining
Digital mapping
Edge joints
Embedding
Feature extraction
Fusion attention network
Geospatial analysis
Global optimization
Image edge detection
Image processing
Image quality
Image reconstruction
Image segmentation
Masks
Modules
multilevel mask generation
multisource data
Navigation
Regularization
regularization algorithm
Remote sensing
road centerline vectorization
road extraction
road surface inference
Roads
Roads & highways
Segmentation
Soft sensors
Spatial data
Trajectory
Transformers
Urban planning
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Automatic extraction of roads from remote sensing imagery has numerous applications, such as urban planning and navigation. However, the quality of images is limited, and most existing road extraction methods suffer from discontinuity. Additionally, there remains a gap between pixel-based road segmentation and road vectorization. To address these challenges, we introduce a method that utilizes a multistage feature fusion attention network (MFFANet) and regularization algorithm to extract road surfaces, centerlines, edges, and intersections. MFFANet comprises three components. The complementary feature embedding module (CFEM) adaptively encodes remote sensing images, vehicle trajectories, and OpenStreetMap (OSM) points to capture specific modal features. The multistage feature fusion module (MFFM) is proposed to improve the completeness and connectivity of road extraction by integrating multisource geospatial features. The multilevel mask generation module (MMGM) enhances road segmentation results through a weighting mechanism that can simultaneously predict local road segments, road sections, and road network masks. Additionally, a novel joint loss function is introduced to balance local and global optimization. In the regularization stage, fused hierarchical masks generate road segments, with a skeleton refinement for centerlines and widths, followed by smooth segment reconstruction and extraction of edges and intersections. Experiments on different datasets demonstrate that our designed fusion attention network outperforms the latest road segmentation models; our regularization algorithm shows strong robustness and the comprehensive metrics of vectorized road line extraction exceeds 70%.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3520610