ERA Dataset

Citation Author(s):
German Aerospace Center & Technical University of Munich
German Aerospace Center & Technical University of Munich
Technical University of Munich
Xiao Xiang
German Aerospace Center & Technical University of Munich
Submitted by:
Yuansheng Hua
Last updated:
Wed, 07/01/2020 - 20:54
Data Format:
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Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance. In this paper, we introduce a novel problem of event recognition in unconstrained aerial videos in the remote sensing community and present a large-scale, human-annotated dataset, named ERA (Event Recognition in Aerial videos), consisting of 2,864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds. The ERA dataset is designed to have a significant intra-class variation and inter-class similarity and captures dynamic events in various circumstances and at dramatically various scales. Moreover, to offer a benchmark for this task, we extensively validate existing deep networks. We expect that the ERA dataset will facilitate further progress in automatic aerial video comprehension. The website is \url{}.


=================  Authors  ===========================

Lichao Mou,

Yuansheng Hua,

Pu Jin,

Xiao Xiang Zhu,


=================  Citation  ===========================

If you use this dataset for your work, please use the following citation:


  title= {{ERA: A dataset and deep learning benchmark for event recognition in aerial videos}},

  author= {Mou, L. and Hua, Y. and Jin, P. and Zhu, X. X.},

  journal= {IEEE Geoscience and Remote Sensing Magazine},

  year= {in press}



==================  Notice!  ===========================

This dataset is ONLY released for academic uses. Please do not further distribute the dataset on other public websites.