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BigEarth

Financiado

Cerrado

Accurate and Scalable Processing of Big Data in Earth Observation

During the last decade, a huge number of earth observation (EO) satellites with optical and Synthetic Aperture Radar sensors onboard have been launched and advances in satellite systems have increased the amount, variety and spati... ver más

31/03/2024

TUB

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

TECHNISCHE UNIVERSITAT BERLIN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2024-03-31

ERC-2017-STG: ERC Starting Grant

I+D

Cerrada hace 8 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

3 Participantes

TUB

1.49M€ | Lider

BURDINBERRI SL

615.56K€ | Participante

UNITN

N.D. | Participante

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Duración del proyecto: 78 meses Fecha Inicio: 2017-09-19
Fecha Fin: 2024-03-31

Líder del proyecto

TECHNISCHE UNIVERSITAT BERLIN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto During the last decade, a huge number of earth observation (EO) satellites with optical and Synthetic Aperture Radar sensors onboard have been launched and advances in satellite systems have increased the amount, variety and spatial/spectral resolution of EO data. This has led to massive EO data archives with huge amount of remote sensing (RS) images, from which mining and retrieving useful information are challenging. In view of that, content based image retrieval (CBIR) has attracted great attention in the RS community. However, existing RS CBIR systems have limitations on: i) characterization of high-level semantic content and spectral information present in RS images, and ii) large-scale RS CBIR problems since their search mechanism is time-demanding and not scalable in operational applications. The BigEarth project aims to develop highly innovative feature extraction and content based retrieval methods and tools for RS images, which can significantly improve the state-of-the-art both in the theory and in the tools currently available. To this end, very important scientific and practical problems will be addressed by focusing on the main challenges of Big EO data on RS image characterization, indexing and search from massive archives. In particular, novel methods and tools will be developed, aiming to: 1) characterize and exploit high level semantic content and spectral information present in RS images; 2) extract features directly from the compressed RS images; 3) achieve accurate and scalable RS image indexing and retrieval; and 4) integrate feature representations of different RS image sources into a unified form of feature representation. Moreover, a benchmark archive with high amount of multi-source RS images will be constructed. From an application point of view, the developed methodologies and tools will have a significant impact on many EO data applications, such as accurate and scalable retrieval of: specific man-made structures and burned forest areas.

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