Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10149
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Main Title: BigEarthNet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding
Author(s): Sumbul, Gencer
Charfuelan, Marcela
Demir, Begüm
Markl, Volker
Type: Image
References: http://dx.doi.org/10.14279/depositonce-9346
Language Code: en
Abstract: The BigEarthNet archive was constructed by the Remote Sensing Image Analysis (RSiM) Group and the Database Systems and Information Management (DIMA) Group at the Technische Universität Berlin (TU Berlin). This work is supported by the European Research Council under the ERC Starting Grant BigEarth and by the German Ministry for Education and Research as Berlin Big Data Center (BBDC). BigEarthNet is a new large-scale Sentinel-2 benchmark archive, consisting of 590,326 Sentinel-2 image patches. To construct BigEarthNet, 125 Sentinel-2 tiles acquired between June 2017 and May 2018 over the 10 countries (Austria, Belgium, Finland, Ireland, Kosovo, Lithuania, Luxembourg, Portugal, Serbia, Switzerland) of Europe were initially selected. All the tiles were atmospherically corrected by the Sentinel-2 Level 2A product generation and formatting tool (sen2cor). Then, they were divided into 590,326 non-overlapping image patches. Each image patch was annotated by the multiple land-cover classes (i.e., multi-labels) that were provided from the CORINE Land Cover database of the year 2018 (CLC 2018). BigEarthNet is significantly larger than the existing archives in remote sensing and opens up promising directions to advance research for the analysis of large-scale remote sensing image archives. It is also very convenient to be used as a training source in the context of deep learning for knowledge discovery from big archives in remote sensing. For the details about BigEarthNet, please see our paper: G. Sumbul, M. Charfuelan, B. Demir, V. Markl, "BigEarthNet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding", IEEE International Geoscience and Remote Sensing Symposium, pp. 5901-5904, Yokohama, Japan, 2019.
URI: https://depositonce.tu-berlin.de/handle/11303/11261
http://dx.doi.org/10.14279/depositonce-10149
Issue Date: 2019
Date Available: 13-Jul-2020
DDC Class: 000 informatics, information science, general works
Subject(s): Sentinel-2 image archive
multi-label image classification
deep neural network
remote sensing
Sponsor/Funder: EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation Fact Sheet/BigEarth
BMBF, 01IS14013A, Verbundprojekt: BBDC - Berliner Kompetenzzentrum für Big Data
License: https://cdla.io/permissive-1-0/
Appears in Collections:FG Remote Sensing Image Analysis Group » Research Data

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