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

Aerial scene recognition is a fundamental research problem in interpretinghigh-resolution aerial imagery. Over the past few years, most studies focus onclassifying an image into one scene category, while in real-world scenarios, itis more often that a single image contains multiple scenes. Therefore, in thispaper, we investigate a more practical yet underexplored task -- multi-scenerecognition in single images. To this end, we create a large-scale dataset,called MultiScene, composed of 100,000 unconstrained high-resolution aerialimages. Considering that manually labeling such images is extremely arduous, weresort to low-cost annotations from crowdsourcing platforms, e.g.,OpenStreetMap (OSM). However, OSM data might suffer from incompleteness andincorrectness, which introduce noise into image labels. To address this issue,we visually inspect 14,000 images and correct their scene labels, yielding asubset of cleanly-annotated images, named MultiScene-Clean. With it, we candevelop and evaluate deep networks for multi-scene recognition using cleandata. Moreover, we provide crowdsourced annotations of all images for thepurpose of studying network learning with noisy labels. We conduct experimentswith extensive baseline models on both MultiScene-Clean and MultiScene to offerbenchmarks for multi-scene recognition in single images and learning from noisylabels for this task, respectively. To facilitate progress, we make our datasetand trained models available onhttps://gitlab.lrz.de/ai4eo/reasoning/multiscene.

MultiScene: A Large-Scale Dataset and Benchmark for Multiscene Recognition in Single Aerial Images