Automatic detection of earthquake-induced ground failure effects through Faster R-CNN deep learning-based object detection using satellite images

Author:

Hacıefendioğlu KemalORCID,Başağa Hasan Basri,Demir Gökhan

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology

Reference27 articles.

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2. Baik H, Son YS, Kim KE (2019) Detection of liquefaction phenomena from the 2017 Pohang (Korea) earthquake using remote sensing data. Remote Sens 11:1–13. https://doi.org/10.3390/rs11182184

3. Fujiwara S, Nakano T, Morishita Y et al (2019) Detection and interpretation of local surface deformation from the 2018 Hokkaido Eastern Iburi Earthquake using ALOS-2 SAR data. Earth Planets Sp. https://doi.org/10.1186/s40623-019-1046-2

4. Girshick R (2015) Fast R-CNN. Proc IEEE Int Conf Comput Vis 2015 Inter. https://doi.org/10.1109/ICCV.2015.169

5. Google Earth (2019) Maxar Technologies CNES/Airbus TerraMetrics. In: https://earth.google.com/web/@-0.8193728,119.87225073,-0.19889318a,46520.77114275d,35y,0h,0t,0r. https://earth.google.com/web/@-0.8193728,119.87225073,-0.19889318a,46520.77114275d,35y,0h,0t,0r. Accessed 6 Apr 2020

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