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

\begin{abstract} The advent of multitemporal high resolution data, like theCopernicus Sentinel-2, has enhanced significantly the potential of monitoringthe earth's surface and environmental dynamics. In this paper, we present anovel deep learning framework for urban change detection which combinesstate-of-the-art fully convolutional networks (similar to U-Net) for featurerepresentation and powerful recurrent networks (such as LSTMs) for temporalmodeling. We report our results on the recently publicly available bi-temporalOnera Satellite Change Detection (OSCD) Sentinel-2 dataset, enhancing thetemporal information with additional images of the same region on differentdates. Moreover, we evaluate the performance of the recurrent networks as wellas the use of the additional dates on the unseen test-set using an ensemblecross-validation strategy. All the developed models during the validation phasehave scored an overall accuracy of more than 95%, while the use of LSTMs andfurther temporal information, boost the F1 rate of the change class by anadditional 1.5%.

Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data