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

Using High Resolution Optical Imagery to Detect Earthquake-Induced Liquefaction: The 2011 Christchurch Earthquake

(2020) Vahid Rashidian et al.   Remote Sensing

An Artificial Intelligence Application for Post-Earthquake Damage Mapping in Palu, Central Sulawesi, Indonesia

(2019) Mutiara Syifa et al.   SENSORS

Detection and interpretation of local surface deformation from the 2018 Hokkaido Eastern Iburi Earthquake using ALOS-2 SAR data

(2019) Satoshi Fujiwara et al.   EARTH PLANETS AND SPACE

Detection of Liquefaction Phenomena from the 2017 Pohang (Korea) Earthquake Using Remote Sensing Data

(2019) Baik et al.   Remote Sensing

Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment

(2019) Maher Ibrahim Sameen et al.   CATENA

Coherence Difference Analysis of Sentinel-1 SAR Interferogram to Identify Earthquake-Induced Disasters in Urban Areas

(2018) Chih-Heng Lu et al.   Remote Sensing

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

(2017) Shaoqing Ren et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Deep learning

(2015) Yann LeCun et al.   NATURE

Documenting Earthquake-Induced Liquefaction Using Satellite Remote Sensing Image Transformations

(2013) T. Oommen et al.   ENVIRONMENTAL & ENGINEERING GEOSCIENCE