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

With the acceleration of the urban expansion, urban change detection (UCD),as a significant and effective approach, can provide the change informationwith respect to geospatial objects for dynamical urban analysis. However,existing datasets suffer from three bottlenecks: (1) lack of high spatialresolution images; (2) lack of semantic annotation; (3) lack of long-rangemulti-temporal images. In this paper, we propose a large scale benchmarkdataset, termed Hi-UCD. This dataset uses aerial images with a spatialresolution of 0.1 m provided by the Estonia Land Board, including three-timephases, and semantically annotated with nine classes of land cover to obtainthe direction of ground objects change. It can be used for detecting andanalyzing refined urban changes. We benchmark our dataset using some classicmethods in binary and multi-class change detection. Experimental results showthat Hi-UCD is challenging yet useful. We hope the Hi-UCD can become a strongbenchmark accelerating future research.

Hi-UCD: A Large-scale Dataset for Urban Semantic Change Detection in Remote Sensing Imagery