Pedram Ghamisi, Omid Ghorbanzadeh, Yonghao Xu, Pedro Herruzo, David Kreil, Michael Kopp, and Sepp Hochreiter

Dataset example

An example of image patches in the Landslide4Sense training dataset.

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 highlevel 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 presents findings from the first globally distributed, multi-sensor landslide detection competition, named as Landslide4Sense.

CEUR Workshop Proceedings, 3207, 2022-07-25.

View paper
IARAI Authors
Dr. Pedram Ghamisi, Omid Ghorbanzadeh, Yonghao Xu, Pedro Herruzo, Dr David Kreil, Dr Michael Kopp​, Dr Sepp Hochreiter
Earth Observation
Benchmark Dataset, Competition, Deep Learning, Landslide Detection, Remote Sensing, Semantic Segmentation


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