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

## TL;DR

This paper presents a CNN-based system utilizing Sentinel-2 satellite imagery for landslide detection and prediction, aiming to enhance hazard assessment and disaster response through scalable, accurate classification models.

## Contribution

It introduces a novel application of CNNs with satellite data for landslide risk classification, filling a gap in existing research and demonstrating improved accuracy over baseline methods.

## Key findings

- CNN models outperform baseline accuracy in landslide detection
- Data augmentation enhances model robustness
- Satellite imagery can effectively support landslide risk assessment

## Abstract

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.