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So2Sat

Financiado

Big Data for 4D Global Urban Mapping 10 16 Bytes from Social Media to EO Satel...

By 2050, around three quarters of the world’s population will live in cities. The new dimension of ongoing global migration into the cities poses fundamental challenges to our societies across the globe. Despite of increasing effo... ver más

31/01/2023

TUM

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

TECHNISCHE UNIVERSITAET MUENCHEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-01-31

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2016-STG: ERC Starting Grant

I+D

Cerrada hace 9 años

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3 Participantes

TUM

1.44M€ | Lider

M. TORRES OLVEGA INDUSTRIAL

543.07K€ | Participante

DLR

56.25K€ | Participante

Últimas noticias

02-12-2024: AGENCIA-VALENCIANA-D... En las últimas 48 horas el Organismo AGENCIA VALENCIANA D... ha otorgado 1 concesiones

02-12-2024: CDTI En las últimas 48 horas el Organismo CDTI ha otorgado 4 concesiones

01-12-2024: REDES En las últimas 48 horas el Organismo REDES ha otorgado 1337 concesiones

Duración del proyecto: 71 meses Fecha Inicio: 2017-02-14
Fecha Fin: 2023-01-31

Líder del proyecto

TECHNISCHE UNIVERSITAET MUENCHEN

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto By 2050, around three quarters of the world’s population will live in cities. The new dimension of ongoing global migration into the cities poses fundamental challenges to our societies across the globe. Despite of increasing efforts, global urban mapping still drags behind the geometric, thematic and temporal resolutions of geo-information needed to address these challenges. Nowadays diverse sets of incomplete data exist. For example, Earth observation (EO) satellites reliably provide geodetically accurate large scale geo-information of the cities on a routine basis from space. But the data availability is limited by resolutions and acquisition geometries of the sensors. Complementarily, massive imagery, text messages and GIS data from open sources and social media are temporally quasi-seamless, spatially multi-perspective, but with diversely unknown qualities. With So2Sat I will jointly exploit big data from social media and satellite observations for global urban mapping, and aim at breakthroughs in 3D/4D urban modelling, infrastructure occupancy classification, and very high resolution population density mapping on a global scale for revolutionizing urban geographic research. The following methodological and application objectives will be addressed: improving urban-related information retrieval from EO satellite data (MO1), mining urban imagery and text messages from social media data (MO2), information fusion from heterogeneous data sources (MO3), big data processing (MO4), as well as pilot applications in informal settlements classification (AO1) and global population density estimation (AO2). The outcome of So2Sat will be the first and unique global and consistent spatial data set on urban morphology (3D/4D) of settlements, and a multidisciplinary application derivate assessing population density. This is seen as a giant leap for urban geography research as well as for formation of opinions for stakeholders based on resilient data.

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