=Paper=
{{Paper
|id=Vol-3207/abtract
|storemode=property
|title=The Landslide4Sense Competition 2022
|pdfUrl=https://ceur-ws.org/Vol-3207/abstract.pdf
|volume=Vol-3207
|authors=Pedram Ghamisi,Omid Ghorbanzadeh,Yonghao Xu,Pedro Herruzo,David Kreil,Michael Kopp,Sepp Hochreiter
|dblpUrl=https://dblp.org/rec/conf/cdceo/GhamisiGXHK0H22
}}
==The Landslide4Sense Competition 2022==
https://ceur-ws.org/Vol-3207/abstract.pdf
The Landslide4Sense Competition 2022
Pedram Ghamisi1,2 , Omid Ghorbanzadeh1 , Yonghao Xu1 , Pedro Herruzo1 ,
David Kreil1 , Michael Kopp1 and Sepp Hochreiter1,3
1
Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria
2
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Machine Learning
Group, Chemnitzer Str. 40, 09599 Freiberg, Germany
3
ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University, 4040 Linz, Austria
Recent advances in computer vision and the high availability of Earth Observation (EO)
imaging have enabled the generation of information about natural hazards. Detecting areas
affected by natural hazards is of obvious immediate importance. The EO images are the main
source of spatial information from hazard impacts in remote and large-scale areas. Modern deep
learning methods have recently automated EO image processing to produce applicable high-
level information. In particular, these methods are preferred over longstanding physics-based
conventional solutions for detecting the natural hazard of landslides. The updated knowledge
of ground surface deformations caused by landslides developed from EO images and machine
learning provides a critical landslide inventory, essential for a better understanding of landslides,
identifying triggers, and identifying prone areas.
A special session of the CDCEO’22 workshop (https://www.iarai.ac.at/cdceo22/) presents
findings from the first globally distributed, multi-sensor landslide detection competition, named
as Landslide4Sense (https://www.iarai.ac.at/landslide4sense/). The aim of the competition is to
promote innovative algorithms for automatic landslide detection using remote sensing images
around the globe, and to provide objective and fair comparisons among different methods.
For this goal, the images are collected from diverse geographical regions offering a unique
benchmark dataset [1], which is an essential resource for conducting interdisciplinary research
in remote sensing, computer vision, and machine learning. The Landslide4Sense data consist of
the training, validation, and test sets, containing 3799, 245, and 800 image patches, respectively.
Each image patch is a composite of 14 bands that include:
• Multispectral data from Sentinel-2: B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12.
• Slope data from ALOS PALSAR: B13
• Digital elevation model (DEM) from ALOS PALSAR: B14
All bands in the competition dataset are resized to the resolution of 10m per pixel (see Fig. 1).
The image patches have the size of 128 × 128 pixels and are labeled in a pixel-wise manner.
CDCEO 2022: 2nd Workshop on Complex Data Challenges in Earth Observation, July 25, 2022, Vienna, Austria
Envelope-Open p.ghamisi@gmail.com (P. Ghamisi); omid.ghorbanzadeh@iarai.ac.at (O. Ghorbanzadeh); yonghao.xu@iarai.ac.at
(Y. Xu); pedro.herruzo@iarai.ac.at (P. Herruzo); david.kreil@iarai.ac.at (D. Kreil); michael.kopp@iarai.ac.at
(M. Kopp); hochreit@ml.jku.at (S. Hochreiter)
Orcid 0000-0003-1203-741X (P. Ghamisi); 0000-0002-9664-8770 (O. Ghorbanzadeh); 0000-0002-6857-0152 (Y. Xu);
0000-0001-7538-2056 (D. Kreil); 0000-0002-1385-1109 (M. Kopp); 0000-0001-7449-2528 (S. Hochreiter)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
Band 1 Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 Band 8 Band 9 Band 10 Band 11 Band 12 Band 13 Band 14 Ground Truth
Figure 1: Illustration of each single layer in the 128 × 128 window size patches of the collected landslide
data set. Bands 1–12 belong to the multi-spectral data from Sentinel-2 and bands 13–14 are slope and
DEM data from ALOS PALSAR. The patches in the last column are corresponding labels.
The competition ranking is based on a quantitative accuracy metric (F1 score) computed with
respect to undisclosed test samples. One special prize was also considered for the creative and
innovative solution in landslide detection according to the evaluation of the Landslide4Sense
scientific committee.
A total of 7775 landslide detection results were submitted to the Landslide4Sense competition
website by 439 unique users within 85 teams https://www.iarai.ac.at/landslide4sense/challenge/.
There were a total of 219 landslide detection results submitted by 29 teams during the test
phase, with a maximum of ten submissions per team allowed. The competitors were from
37 different countries or regions around the world, such as mainland China, Hong Kong, the
USA, Germany, Austria, Japan, Canada, and Australia. This competition had four winning
teams. The first three winning teams achieved the highest F1 scores on the test phase. One
more team was selected for the special prize for their creative and innovative solution in
landslide detection according to the evaluation of the Landslide4Sense scientific committee. The
Landslide4Sense competition outcome paper describes the innovative algorithms for automatic
landslide detection introduced by these winning teams (https://arxiv.org/abs/2209.02556) [2].
Data is available at Future Development Leaderboard for ongoing evaluation at https://www.
iarai.ac.at/landslide4sense/challenge/, and anyone is invited to submit more landslide detection
results to check the accuracy of their methods against those of others.
References
[1] O. Ghorbanzadeh, Y. Xu, P. Ghamisi, M. Kopp, D. Kreil, Landslide4Sense: Reference bench-
mark data and deep learning models for landslide detection, arXiv preprint arXiv:2206.00515
(2022).
[2] O. Ghorbanzadeh, Y. Xu, H. Zhao, J. Wang, Y. Zhong, D. Zhao, Q. Zang, S. Wang, F. Zhang,
Y. Shi, X. X. Zhu, L. Bai, W. Li, W. Peng, P. Ghamisi, The outcome of the 2022 Landslide4Sense
competition: Advanced landslide detection from multi-source satellite imagery, arXiv
preprint arXiv:2209.02556 (2022).