Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method
for Road Updating from Remote Sensing Imagery and Historical Vector Maps
- URL: http://arxiv.org/abs/2304.14972v1
- Date: Fri, 28 Apr 2023 16:51:35 GMT
- Title: Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method
for Road Updating from Remote Sensing Imagery and Historical Vector Maps
- Authors: Xin Chen, Anzhu Yu, Qun Sun, Wenyue Guo, Qing Xu and Bowei Wen
- Abstract summary: We propose a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications.
The proposed SRUNet can provide stable, up-to-date, and reliable prediction results for a wide range of road renewal tasks.
- Score: 3.350048575501172
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A road is the skeleton of a city and is a fundamental and important
geographical component. Currently, many countries have built geo-information
databases and gathered large amounts of geographic data. However, with the
extensive construction of infrastructure and rapid expansion of cities,
automatic updating of road data is imperative to maintain the high quality of
current basic geographic information. However, obtaining bi-phase images for
the same area is difficult, and complex post-processing methods are required to
update the existing databases.To solve these problems, we proposed a road
detection method based on semi-supervised learning (SRUNet) specifically for
road-updating applications; in this approach, historical road information was
fused with the latest images to directly obtain the latest state of the
road.Considering that the texture of a road is complex, a multi-branch network,
named the Map Encoding Branch (MEB) was proposed for representation learning,
where the Boundary Enhancement Module (BEM) was used to improve the accuracy of
boundary prediction, and the Residual Refinement Module (RRM) was used to
optimize the prediction results. Further, to fully utilize the limited amount
of label information and to enhance the prediction accuracy on unlabeled
images, we utilized the mean teacher framework as the basic semi-supervised
learning framework and introduced Regional Contrast (ReCo) in our work to
improve the model capacity for distinguishing between the characteristics of
roads and background elements.We applied our method to two datasets. Our model
can effectively improve the performance of a model with fewer labels. Overall,
the proposed SRUNet can provide stable, up-to-date, and reliable prediction
results for a wide range of road renewal tasks.
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