English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In order to improve the cross-domain applicability of road segmentation, a feature transfer based adversarial domain adaptation method is presented for cross-domain road extraction. The presented method consists of two main parts, a feature transfer network and an adversarial domain adaption network. The feature transfer network transforms the images of the source domain into the feature space of the target domain. The adversarial domain adaption network learns to distinguish whether the results come from source domain or target domain, to promote the backbone network of road extraction to learn the common features of the road. The experimental results showed that the proposed method improved the generalization ability of the road extraction network and could extract the road target from cross-domain images accurately and effectively. The proposed method can realize the road extraction across domain without any annotation of target domain, so it has good application value.

Feature transfer based adversarial domain adaptation method for cross-domain road extraction