Author Identifier (ORCID)
Abstract
Extracting roads accurately from remote sensing images is important for map updates, traffic analysis, and infrastructure monitoring. Medium-resolution multispectral images can provide useful surface and background information, but when used alone, the spatial details are limited for retaining narrow roads, intersection structures, and fine road topologies. To address this problem, this paper proposes GeoRoad-UPerNet, a Geo-1-centered weakly supervised multispectral framework for road extraction. In this framework, Geo-1 serves as the primary 16-band multispectral source, Sentinel-2 Level-2A imagery serves as auxiliary contextual support, and OpenStreetMap (OSM) road information is converted into proxy supervision rather than dense manual ground truth. GeoRoad-UPerNet contains three modules: a Geo Spectral Semantic Stem (GSSS), a Geo-Auxiliary Gated Fusion module (GAGF), and a Road Semantic Multi-Task Head (RSMH). GSSS strengthens road-sensitive multispectral responses in the Geo-1 branch. GAGF injects Sentinel-2 context through a Geo-centered gate instead of symmetric channel concatenation. RSMH imposes restrained hierarchy- and material-aware semantic regularization on the shared decoder representation during training. On the fixed source-domain benchmark, the complete model achieves an IoU of 0.7204, an F1-score of 0.8375, a Precision of 0.8092, and a Recall of 0.8678 against OSM-derived proxy masks. Relative to the UPerNet-MiT-B3 early-fusion baseline, IoU, F1-score, and Precision increase by 6.29%, 3.65%, and 12.58%, respectively. These results indicate that role-aware multisource organization improves road extraction under proxy supervision and reduces boundary noise and background false positives.
Keywords
feature fusion, geo-1 multispectral imagery, road extraction, semantic regularization, weak supervision
Document Type
Journal Article
Date of Publication
6-1-2026
Article Number
1745
Volume
18
Issue
11
Publication Title
Remote Sensing
Publisher
MDPI
School
School of Business and Law
Funding Information
This research was funded by the National Natural Science Foundation of China, grant numbers 62076224 and 42230208.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Chen, S., Chen, Y., Li, J., & Yang, A. (2026). GeoRoad-UPerNet: Geo-1-based weakly supervised multispectral road extraction via role-aware context fusion and semantic regularization. Remote Sensing, 18(11). https://doi.org/10.3390/rs18111745