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In this paper, the authors aim to combine the latest state of the art modelsin image recognition with the best publicly available satellite images tocreate a system for landslide risk mitigation. We focus first on landslidedetection and further propose a similar system to be used for prediction. Suchmodels are valuable as they could easily be scaled up to provide data forhazard evaluation, as satellite imagery becomes increasingly available. Thegoal is to use satellite images and correlated data to enrich the publicrepository of data and guide disaster relief efforts for locating precise areaswhere landslides have occurred. Different image augmentation methods are usedto increase diversity in the chosen dataset and create more robustclassification. The resulting outputs are then fed into variants of 3-Dconvolutional neural networks. A review of the current literature indicatesthere is no research using CNNs (Convolutional Neural Networks) and freelyavailable satellite imagery for classifying landslide risk. The model has shownto be ultimately able to achieve a significantly better than baseline accuracy.

Landslide Geohazard Assessment With Convolutional Neural Networks Using Sentinel-2 Imagery Data