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

The machine learning community has recently had increased interest in theclimate and disaster damage domain due to a marked increased occurrences ofnatural hazards (e.g., hurricanes, forest fires, floods, earthquakes). However,not enough attention has been devoted to mitigating probable destruction fromimpending natural hazards. We explore this crucial space by predictingbuilding-level damages on a before-the-fact basis that would allow state actorsand non-governmental organizations to be best equipped with resourcedistribution to minimize or preempt losses. We introduce PreDisM that employsan ensemble of ResNets and fully connected layers over decision trees tocapture image-level and meta-level information to accurately estimate weaknessof man-made structures to disaster-occurrences. Our model performs well and isresponsive to tuning across types of disasters and highlights the space ofpreemptive hazard damage modelling.

PreDisM: Pre-Disaster Modelling With CNN Ensembles for At-Risk Communities