  In this paper, the authors aim to combine the latest state of the art models
in image recognition with the best publicly available satellite images to
create a system for landslide risk mitigation. We focus first on landslide
detection and further propose a similar system to be used for prediction. Such
models are valuable as they could easily be scaled up to provide data for
hazard evaluation, as satellite imagery becomes increasingly available. The
goal is to use satellite images and correlated data to enrich the public
repository of data and guide disaster relief efforts for locating precise areas
where landslides have occurred. Different image augmentation methods are used
to increase diversity in the chosen dataset and create more robust
classification. The resulting outputs are then fed into variants of 3-D
convolutional neural networks. A review of the current literature indicates
there is no research using CNNs (Convolutional Neural Networks) and freely
available satellite imagery for classifying landslide risk. The model has shown
to be ultimately able to achieve a significantly better than baseline accuracy.
