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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This work proposes methodologies aimed at evaluating the damage occurred in the Amatrice town by using optical and Synthetic Aperture Radar (SAR) change features obtained from satellite images. The objective is to achieve a damage map employing the satellite change features in a classifier algorithm, namely the Features Stepwise Thresholding (FST) method. The main novelties of the proposed analysis concern the estimation of derived features at object scale and the exploitation of the unsupervised FST algorithm. A segmentation of the study area into several buildings blocks has been done by considering a set of polygons, over the Amatrice town, extracted from the open source Open Street Map (OSM) geo-database.\udThe available satellite dataset is composed of several optical and SAR images, collected before and after the seismic event.\udRegarding the optical data, we selected the Normalised Difference Index (NDI), and two quantities com-ing from the Information Theory, namely the Kullback-Libler Divergence (KLD) and the Mutual Information (MI). In addition, for the SAR data we picked out the Intensity Correlation Difference (ICD) and the KLD parameter. The exploitation of these features in the FST algorithm permits to obtain a plausible damage map that is able to indicate the most affected areas

A multisensor approach for the 2016 Amatrice earthquake damage assessment