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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we revisit the problem of classifying ships (maritime vessels)detected from overhead imagery. Despite the last decade of research on thisvery important and pertinent problem, it remains largely unsolved. One of themajor issues with the detection and classification of ships and other objectsin the maritime domain is the lack of substantial ground truth data needed totrain state-of-the-art machine learning algorithms. We address this issue bybuilding a large (200k) synthetic image dataset using the Unity gaming engineand 3D ship models. We demonstrate that with the use of synthetic data,classification performance increases dramatically, particularly when there arevery few annotated images used in training.

Ship classification from overhead imagery using synthetic data and domain adaptation