Explore the words cloud of the BigEarth project. It provides you a very rough idea of what is the project "BigEarth" about.
The following table provides information about the project.
TECHNISCHE UNIVERSITAT BERLIN
|Coordinator Country||Germany [DE]|
|Total cost||1˙491˙479 €|
|EC max contribution||1˙491˙479 € (100%)|
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
|Duration (year-month-day)||from 2018-04-01 to 2023-03-31|
Take a look of project's partnership.
|1||TECHNISCHE UNIVERSITAT BERLIN||DE (BERLIN)||coordinator||1˙491˙479.00|
|2||UNIVERSITA DEGLI STUDI DI TRENTO||IT (TRENTO)||participant||0.00|
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.
|year||authors and title||journal||last update|
Mohamed Lamine Mekhalfi, Mesay Belete Bejiga, Davide Soresina, Farid Melgani, BegÃ¼m Demir
Capsule Networks for Object Detection in UAV Imagery
published pages: 1694, ISSN: 2072-4292, DOI: 10.3390/rs11141694
|Remote Sensing 11/14||2019-11-26|
Thomas Reato, Begum Demir, Lorenzo Bruzzone
An Unsupervised Multicode Hashing Method for Accurate and Scalable Remote Sensing Image Retrieval
published pages: 276-280, ISSN: 1545-598X, DOI: 10.1109/lgrs.2018.2870686
|IEEE Geoscience and Remote Sensing Letters 16/2||2019-04-18|
Osman Emre Dai, Begum Demir, Bulent Sankur, Lorenzo Bruzzone
A Novel System for Content-Based Retrieval of Single and Multi-Label High-Dimensional Remote Sensing Images
published pages: 2473-2490, ISSN: 1939-1404, DOI: 10.1109/jstars.2018.2832985
|IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11/7||2019-04-18|
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The information about "BIGEARTH" are provided by the European Opendata Portal: CORDIS opendata.