Crowdsourcing to enhance insights from satellite observations

MAZUMDAR, Suvodeep, WRIGLEY, S, CIRAVEGNA, F, PELLOQUIN, C, CHAPMAN, S, DE VENDECTIS, L, GRANDONI, D, FERRI, M and BOLOGNINI, L (2018). Crowdsourcing to enhance insights from satellite observations. In: BORDOGNA, Gloria and CARRARA, Paola, (eds.) Mobile information systems leveraging volunteered geographic information for Earth observation. Earth Systems Data and Models (4). Cham, Springer, 35-52. (In Press)

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Abstract

Insights from satellite observations are increasingly being used to enhance a range of domains from highly specialised scientific research through to everyday applications directly benefiting members of the public. A particular category of satellite observations—Earth Observations (EO)—is concerned with capturing information regarding the Earth’s atmospheric and environmental conditions and observing human activity and its impact on the Earth’s surface. A growing number of technologies and services heavily rely on EO data and the rapidly improving fidelity, coverage, timeliness and accessibility of such observations are providing significant opportunities for new applications of economic and societal benefit. With the increasing importance, relevance and size of EO data sets, it is critical to understand how the value of such data can be maximised by complementing EO with other sources of data and efficiently making complex interpretations and decisions. The wide adoption and availability of smartphones, Internet devices and increased accessibility to information has paved the way for large numbers of citizens and communities to participate in scientific, technological, societal and decision-making activities. This chapter discusses the experience of the European Space Agency funded Crowd4Sat project led by the University of Sheffield that investigated different facets of how crowdsourcing and citizen science impact upon the validation, use and enhancement of Observations from Satellites products and services.

Item Type:	Book Section
Additional Information:	Series ISSN: 2364-5830
Departments - Does NOT include content added after October 2018:	Faculty of Science, Technology and Arts > Department of Computing
Identification Number:	https://doi.org/10.1007/978-3-319-70878-2_2
Page Range:	35-52
Depositing User:	Suvodeep Mazumdar
Date Deposited:	17 Jan 2018 11:58
Last Modified:	18 Mar 2021 16:31
URI:	https://shura.shu.ac.uk/id/eprint/16917

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