The U. V. Helava Award

Best Paper for the period 2020-2021


The U.V. Helava Award, sponsored by Elsevier B.V. and Leica Geosystems AG, is a prestigious ISPRS Award, which was established in 1998 to encourage and stimulate submission of high-quality scientific papers by individual authors or groups to the ISPRS Journal, to promote and advertise the Journal, and to honour the outstanding contributions of Dr. Uuno V. Helava to research and development in photogrammetry and remote sensing.

The Award is presented to authors of the best paper, written in English and published exclusively in the ISPRS Journal during the four-year period from January of a Congress year to December of the year prior to the next Congress. As the COVID-19 global pandemic has resulted in the postponement of the XXIV ISPRS Congress to 2022 (instead of 2020), it has been decided to select a best paper for the 2020-2021 period. Therefore, the seventh U.V. Helava Award will be presented at the XXIV ISPRS Congress in 2022.

The award consists of a monetary grant of 10,000 SFr., certificates and a silver plaque. It is sponsored by Elsevier B.V. and Hexagon Geosystems, while the Institute of Photogrammetry and Remote Sensing (Prof. Henrik Haggrén), Helsinki University of Technology (the University where Helava studied) paid half the costs for the silver plaque. The plaque was designed with care and enthusiasm by the 1980-88 ISPRS Technical Commission III President, Einari Kilpelä, previously professor at the Helsinki University of Technology.

A five-member Jury, comprising experts of high scientific standing, whose expertise covers the main topics included in the scope of the Journal, evaluates the papers. For each year of the evaluation period, the best paper is selected, and among these two papers, the one to receive the U.V. Helava Award. The award-winning paper for 2020-2021 is:

 

"Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion".

by Andrea Meraner a, Patrick Ebel b, Xiao Xiang Zhu b,c and Michael Schmitt d

a Data Science in Easrth Observation, Technical University of Munich, Germany
b Data Science in Earth Observation, Technical University of Munich, Germany
c Remote Sensing Technology Institute, German Aerospace Center (DLR), Germany
d Data Science in Earth Observation, Technical University of Munich, Germany

 

Andrea Meraner Patrick Ebel Xiao Xiang Zhu Michael Schmitt
Andrea Meraner Patrick Ebel Xiao Xiang Zhu Michael Schmitt

 

published in volume 166, August 2020, pp. 333-346,
https://doi.org/10.1016/j.isprsjprs.2020.05.013

 

Jury’s rationale for the paper selection

This paper developed a cloud-removal model based on a deep residual neural network using SAR data and fusion of SAR and optical data. The methodology was tested by using the data across the globe and the seasons. The Jury thinks that the work is truly innovative and has been impactful since its publication. The developed methodology tackled a common problem in optical remote sensing and has a wide application potential. Therefore, it is very deserving of the U.V. Helava Award for 2020-2021.

The seventh U.V. Helava Award will be presented at the XXIV ISPRS Congress in Nice, France, 6-11 June 2022 by Christian Heipke, ISPRS President, and representatives of the sponsors.

On behalf of the ISPRS and the U.V. Helava Award Jury, we would like to congratulate the authors for this distinction and thank them for their contribution. We would also like to thank the sponsors of the Award and the Jury members for their thorough evaluations and dedication to this important process.

 

Qihao Weng, MAE, IEEE/AAAS/AAG/ASPRS/AAIA
Fellow,
Editor-in-Chief,
ISPRS Journal of Photogrammetry and Remote Sensing,
The Hong Kong Polytechnic University, Hong Kong.
E-mail address: qihao.weng@polyu.edu.hk

Clément Mallet, Ph.D.
Editor-in-Chief,
ISPRS Journal of Photogrammetry and Remote Sensing,
Univ. Gustave Eiffel & IGN/ENSG, France.
E-mail address: Clement.Mallet@ign.fr

 

 
Nice 2022
XXIVth ISPRS Congress:

June 6-11, 2022
Nice, FRANCE
 
 
 
 
 
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