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

Since the United Nations launched the Sustainable Development Goals (SDG) in2015, numerous universities, NGOs and other organizations have attempted todevelop tools for monitoring worldwide progress in achieving them. Led byadvancements in the fields of earth observation techniques, data sciences andthe emergence of artificial intelligence, a number of research teams havedeveloped innovative tools for highlighting areas of vulnerability and trackingthe implementation of SDG targets. In this paper we demonstrate thatindividuals with no organizational affiliation and equipped only with commonhardware, publicly available datasets and cloud-based computing services canparticipate in the improvement of predicting machine-learning-based approachesto predicting local poverty levels in a given agro-ecological environment. Theapproach builds upon several pioneering efforts over the last five yearsrelated to mapping poverty by deep learning to process satellite imagery and"ground-truth" data from the field to link features with incidence of povertyin a particular context. The approach employs new methods for objectidentification in order to optimize the modeled results and achievesignificantly high accuracy. A key goal of the project was to intentionallykeep costs as low as possible - by using freely available resources - so thatcitizen scientists, students and organizations could replicate the method inother areas of interest. Moreover, for simplicity, the input data used werederived from just a handful of sources (involving only earth observation andpopulation headcounts). The results of the project could therefore certainly bestrengthened further through the integration of proprietary data from socialnetworks, mobile phone providers, and other sources.

Seeing poverty from space, how much can it be tuned?