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

Water events are the most frequent and costliest climate disasters around theworld. In the U.S., an estimated 127 million people who live in coastal areasare at risk of substantial home damage from hurricanes or flooding. In floodemergency management, timely and effective spatial decision-making andintelligent routing depend on flood depth information at a fine spatiotemporalscale. In this paper, crowdsourcing is utilized to collect photos of submergedstop signs, and pair each photo with a pre-flood photo taken at the samelocation. Each photo pair is then analyzed using deep neural network and imageprocessing to estimate the depth of floodwater in the location of the photo.Generated point-by-point depth data is converted to a flood inundation map andused by an A* search algorithm to determine an optimal flood-free pathconnecting points of interest. Results provide crucial information to rescueteams and evacuees by enabling effective wayfinding during flooding events.

Feasibility study of urban flood mapping using traffic signs for route optimization